The relationship between loneliness and depression among college students: Mining data derived from passive sensing

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dc.contributor.authorQirtas, Malik Muhammad
dc.contributor.authorZafeiridi, Evi
dc.contributor.authorWhite, Eleanor Bantry
dc.contributor.authorPesch, Dirk
dc.contributor.funderScience Foundation Ireland(SFI), SFI, 18/CRT/6222
dc.date.accessioned2026-03-11T09:50:02Z
dc.date.available2026-03-11T09:50:02Z
dc.date.issued2023-01-01
dc.description.abstractBackground: While there is recognition of the relationship between loneliness and depression, specific behavioural patterns distinguishing both are still not well understood. Objectives: Our objective is to identify distinct behavioural patterns of loneliness and depression from a passively collected dataset of college students, understand their similarities and interrelationships and assess their effectiveness in identifying loneliness and depression. Methods: Utilizing the StudentLife dataset, we applied regression analysis to determine associations with self-reported loneliness and depression. Mediation analysis interprets the relationship between the two conditions, and machine learning models predict loneliness and depression based on behavioural indicators. Results: Distinct behavioural patterns emerged: high evening screen time (OR = 1.45, p = 0.002) and high overall phone usage (OR = 1.50, p = 0.003) were associated with more loneliness, whereas depression was significantly associated with fewer screen unlocks (OR = 0.75, p = 0.044) and visits to fewer unique places (OR = 0.85, p = 0.023). Longer durations of physical activity (OR = 0.72, p = 0.014) and sleep (OR = 0.46, p = 0.002) are linked to a lower risk of both loneliness and depression. Mediation analysis revealed that loneliness significantly increases the likelihood of depression by 48%. The prediction accuracy of our XGBoost-based machine learning approach was 82.43% for loneliness and 79.43% for depression. Conclusion: Our findings show that high evening screen time and overall phone usage are significantly associated with increased loneliness, while fewer screen unlocks and visits to fewer unique places are significantly related to depression. The findings can help in developing targeted interventions to promote well-being and mental health in students.en
dc.description.sponsorshipThis publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 18/CRT/6222.
dc.description.versionPublished Version
dc.format.extent15
dc.format.mimetypeapplication/pdfen
dc.identifier.authororcidQirtas, Malik Muhammad
dc.identifier.authororcidZafeiridi, Evi
dc.identifier.authororcidWhite, Eleanor Bantry§0000-0002-7663-6836
dc.identifier.authororcidPesch, Dirk§0000-0001-9706-5705
dc.identifier.citationQirtas, M M, Zafeiridi, E, White, E B & Pesch, D 2023, 'The relationship between loneliness and depression among college students : Mining data derived from passive sensing', Digital Health, vol. 9, pp. 1-15. https://doi.org/10.1177/20552076231211104
dc.identifier.doi10.1177/20552076231211104
dc.identifier.endpage15
dc.identifier.issn2055-2076
dc.identifier.journaltitleDigital Health
dc.identifier.otherORCID: /0000-0001-9706-5705/work/197430750
dc.identifier.otherORCID: /0000-0002-7663-6836/work/198101814
dc.identifier.otherArXiv: http://arxiv.org/abs/2308.15509v1
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/18618
dc.identifier.volume9
dc.language.isoen
dc.publisherSAGE Publications Inc.
dc.rights© 2023, the Authors. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage)
dc.rights.accessrightsopen access
dc.rights.licensenameAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.statusPeer reviewed
dc.subjectDepression
dc.subjectFitness tracker
dc.subjectLoneliness
dc.subjectPassive sensing
dc.subjectSmartphone
dc.subjectStudents
dc.titleThe relationship between loneliness and depression among college students: Mining data derived from passive sensingen
dc.typeArticle (peer-reviewed)
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