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Identifying daily-living features related to loneliness: A causal machine learning approach

Wang, Yuning; Auxier, Jennifer; Amayag, Mark; Azimi, Iman; Rahmani, Amir M.; Liljeberg, Pasi; Axelin, Anna

Identifying daily-living features related to loneliness: A causal machine learning approach

Wang, Yuning
Auxier, Jennifer
Amayag, Mark
Azimi, Iman
Rahmani, Amir M.
Liljeberg, Pasi
Axelin, Anna
Katso/Avaa
journal.pone.0336287.pdf (675.0Kb)
Lataukset: 

Public Library of Science (PLoS)
doi:10.1371/journal.pone.0336287
URI
https://doi.org/10.1371/journal.pone.0336287
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Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe202601215548
Tiivistelmä

Background

Loneliness is a distressing feeling that influences well-being. Immigrants’ experience of acculturation to a new dominant culture places them at risk for maladaptive behaviors and daily rhythms leading to loneliness. Identifying daily-living features that causally influence loneliness is essential for developing effective preventive mental health screening.


Objective

To identify the important daily living-features related to loneliness for the development of robust screening solutions using causal machine learning for health providers working with first-generation immigrants.


Methods

We monitored 39 immigrants in Finland for 28 days using mobile devices and wearables under free-living conditions. Data included ecological momentary assessments of loneliness, social interactions, physical activity, sleep, and cardiac features. We estimated the average treatment effect (ATE) of each daily-living feature (treatment variable) on loneliness scores (outcome) and validated the robustness of causal estimates using three refutation techniques.


Results

Our results reveal the ATE of various daily-living features on loneliness. Features such as longer outgoing call durations (ATE = 0.197, p < 0.001), higher LF/HF ratio (ATE = 0.129, p < 0.0001), higher respiratory rate (ATE = 0.144, p < 0.001), and increased inactivity (ATE = 0.130, p < 0.001) causally increased loneliness. Conversely, certain features exhibit negative ATEs, such as higher activity calories (ATE = −0.174, p < 0.001), sleep RMSSD (ATE = −0.128, p < 0.001), longer home duration (ATE = −0.107, p < 0.001), and more sleep time (ATE = −0.103, p < 0.001) mitigated loneliness.


Conclusions

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.

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