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Predicting subjective well-being in a high-risk sample of Russian mental health app users
Despite recent achievements in predicting personality traits and some other human psychological features with digital traces, prediction of subjective well-being (SWB) appears to be a relatively new task with few solutions. COVID-19 pandemic has added both a stronger need for rapid SWB screening and...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8978494/ https://www.ncbi.nlm.nih.gov/pubmed/35402139 http://dx.doi.org/10.1140/epjds/s13688-022-00333-x |
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author | Panicheva, Polina Mararitsa, Larisa Sorokin, Semen Koltsova, Olessia Rosso, Paolo |
author_facet | Panicheva, Polina Mararitsa, Larisa Sorokin, Semen Koltsova, Olessia Rosso, Paolo |
author_sort | Panicheva, Polina |
collection | PubMed |
description | Despite recent achievements in predicting personality traits and some other human psychological features with digital traces, prediction of subjective well-being (SWB) appears to be a relatively new task with few solutions. COVID-19 pandemic has added both a stronger need for rapid SWB screening and new opportunities for it, with online mental health applications gaining popularity and accumulating large and diverse user data. Nevertheless, the few existing works so far have aimed at predicting SWB, and have done so only in terms of Diener’s Satisfaction with Life Scale. None of them analyzes the scale developed by the World Health Organization, known as WHO-5 – a widely accepted tool for screening mental well-being and, specifically, for depression risk detection. Moreover, existing research is limited to English-speaking populations, and tend to use text, network and app usage types of data separately. In the current work, we cover these gaps by predicting both mentioned SWB scales on a sample of Russian mental health app users who represent a population with high risk of mental health problems. In doing so, we employ a unique combination of phone application usage data with private messaging and networking digital traces from VKontakte, the most popular social media platform in Russia. As a result, we predict Diener’s SWB scale with the state-of-the-art quality, introduce the first predictive models for WHO-5, with similar quality, and reach high accuracy in the prediction of clinically meaningful classes of the latter scale. Moreover, our feature analysis sheds light on the interrelated nature of the two studied scales: they are both characterized by negative sentiment expressed in text messages and by phone application usage in the morning hours, confirming some previous findings on subjective well-being manifestations. At the same time, SWB measured by Diener’s scale is reflected mostly in lexical features referring to social and affective interactions, while mental well-being is characterized by objective features that reflect physiological functioning, circadian rhythms and somatic conditions, thus saliently demonstrating the underlying theoretical differences between the two scales. |
format | Online Article Text |
id | pubmed-8978494 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-89784942022-04-04 Predicting subjective well-being in a high-risk sample of Russian mental health app users Panicheva, Polina Mararitsa, Larisa Sorokin, Semen Koltsova, Olessia Rosso, Paolo EPJ Data Sci Regular Article Despite recent achievements in predicting personality traits and some other human psychological features with digital traces, prediction of subjective well-being (SWB) appears to be a relatively new task with few solutions. COVID-19 pandemic has added both a stronger need for rapid SWB screening and new opportunities for it, with online mental health applications gaining popularity and accumulating large and diverse user data. Nevertheless, the few existing works so far have aimed at predicting SWB, and have done so only in terms of Diener’s Satisfaction with Life Scale. None of them analyzes the scale developed by the World Health Organization, known as WHO-5 – a widely accepted tool for screening mental well-being and, specifically, for depression risk detection. Moreover, existing research is limited to English-speaking populations, and tend to use text, network and app usage types of data separately. In the current work, we cover these gaps by predicting both mentioned SWB scales on a sample of Russian mental health app users who represent a population with high risk of mental health problems. In doing so, we employ a unique combination of phone application usage data with private messaging and networking digital traces from VKontakte, the most popular social media platform in Russia. As a result, we predict Diener’s SWB scale with the state-of-the-art quality, introduce the first predictive models for WHO-5, with similar quality, and reach high accuracy in the prediction of clinically meaningful classes of the latter scale. Moreover, our feature analysis sheds light on the interrelated nature of the two studied scales: they are both characterized by negative sentiment expressed in text messages and by phone application usage in the morning hours, confirming some previous findings on subjective well-being manifestations. At the same time, SWB measured by Diener’s scale is reflected mostly in lexical features referring to social and affective interactions, while mental well-being is characterized by objective features that reflect physiological functioning, circadian rhythms and somatic conditions, thus saliently demonstrating the underlying theoretical differences between the two scales. Springer Berlin Heidelberg 2022-04-04 2022 /pmc/articles/PMC8978494/ /pubmed/35402139 http://dx.doi.org/10.1140/epjds/s13688-022-00333-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Regular Article Panicheva, Polina Mararitsa, Larisa Sorokin, Semen Koltsova, Olessia Rosso, Paolo Predicting subjective well-being in a high-risk sample of Russian mental health app users |
title | Predicting subjective well-being in a high-risk sample of Russian mental health app users |
title_full | Predicting subjective well-being in a high-risk sample of Russian mental health app users |
title_fullStr | Predicting subjective well-being in a high-risk sample of Russian mental health app users |
title_full_unstemmed | Predicting subjective well-being in a high-risk sample of Russian mental health app users |
title_short | Predicting subjective well-being in a high-risk sample of Russian mental health app users |
title_sort | predicting subjective well-being in a high-risk sample of russian mental health app users |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8978494/ https://www.ncbi.nlm.nih.gov/pubmed/35402139 http://dx.doi.org/10.1140/epjds/s13688-022-00333-x |
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