Identifying daily-living features related to loneliness: A causal machine learning approach
Daily-living features, including social interactions, activity, sleep, and cardiac features, causally influence loneliness. Our findings provide a basis for loneliness screening targeting immigrant populations. Future work should refine the measurement and incorporate contextual information to establish more reliable causal links in real life.
Authors
- Amir M. Rahmani (ORCID: https://orcid.org/0000-0003-3195-8337)
- Pasi Liljeberg (ORCID: https://orcid.org/0000-0002-9392-3589)
- Jennifer Auxier (ORCID: https://orcid.org/0000-0001-9504-2518)
- Anna Axelin (ORCID: https://orcid.org/0000-0003-2743-3589)
- Iman Azimi (ORCID: https://orcid.org/0000-0001-5003-299X)
- Yuning Wang (ORCID: https://orcid.org/0000-0001-7351-6866)
- Mark Amayag (ORCID: https://orcid.org/0009-0009-8490-3058)
Institutions
- University of British Columbia (CA)
- University of Turku (FI)
- University of California, Irvine (US)
- Irvine University (US)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-12-17
- DOI
- https://doi.org/10.1371/journal.pone.0336287
- Primary Topic
- Health disparities and outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00