The relationship between loneliness and depression among college students: Mining data derived from passive sensing
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Date
2023-01-01
Authors
Qirtas, Malik Muhammad
Zafeiridi, Evi
White, Eleanor Bantry
Pesch, Dirk
Journal Title
Journal ISSN
Volume Title
Publisher
SAGE Publications Inc.
Published Version
Abstract
Background: 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.
Description
Keywords
Depression , Fitness tracker , Loneliness , Passive sensing , Smartphone , Students
Citation
Qirtas, 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