Predicting subjective well-being in a high-risk sample of Russian mental health app users
Abstract 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 scre...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-04-01
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| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1140/epjds/s13688-022-00333-x |
| _version_ | 1819209908889845760 |
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| author | Polina Panicheva Larisa Mararitsa Semen Sorokin Olessia Koltsova Paolo Rosso |
| author_facet | Polina Panicheva Larisa Mararitsa Semen Sorokin Olessia Koltsova Paolo Rosso |
| author_sort | Polina Panicheva |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-23T06:02:46Z |
| format | Article |
| id | doaj.art-79255edfba5746fc864efe03aa26f86c |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-12-23T06:02:46Z |
| publishDate | 2022-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-79255edfba5746fc864efe03aa26f86c2022-12-21T17:57:38ZengSpringerOpenEPJ Data Science2193-11272022-04-0111114310.1140/epjds/s13688-022-00333-xPredicting subjective well-being in a high-risk sample of Russian mental health app usersPolina Panicheva0Larisa Mararitsa1Semen Sorokin2Olessia Koltsova3Paolo Rosso4Laboratory for Social and Cognitive Informatics, HSE UniversityLaboratory for Social and Cognitive Informatics, HSE UniversityLaboratory for Social and Cognitive Informatics, HSE UniversityLaboratory for Social and Cognitive Informatics, HSE UniversityPattern Recognition and Human Language Technology Research Center, Universitat Politècnica de ValènciaAbstract 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.https://doi.org/10.1140/epjds/s13688-022-00333-xDigital tracesSubjective well-beingMental health prediction |
| spellingShingle | Polina Panicheva Larisa Mararitsa Semen Sorokin Olessia Koltsova Paolo Rosso Predicting subjective well-being in a high-risk sample of Russian mental health app users EPJ Data Science Digital traces Subjective well-being Mental health prediction |
| 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 | Digital traces Subjective well-being Mental health prediction |
| url | https://doi.org/10.1140/epjds/s13688-022-00333-x |
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