Evidence map›Paper›PMID 35402139›Full record

ArticleEPJ data science2022

Predicting subjective well-being in a high-risk sample of Russian mental health app users.

Polina Panicheva, Larisa Mararitsa, Semen Sorokin, Olessia Koltsova, Paolo Rosso

Open access · goldAbstract read
In one paragraph

Article in EPJ data science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
3.6field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 19 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 2 countries.

Polina PanichevaLaboratory for Social and Cognitive Informatics, HSE University, Saint Petersburg, Russia.
Larisa MararitsaLaboratory for Social and Cognitive Informatics, HSE University, Saint Petersburg, Russia.
Semen SorokinLaboratory for Social and Cognitive Informatics, HSE University, Saint Petersburg, Russia.
Olessia KoltsovaLaboratory for Social and Cognitive Informatics, HSE University, Saint Petersburg, Russia.ORCID 0000-0002-2669-3154
Paolo RossoPattern Recognition and Human Language Technology Research Center, Universitat Politècnica de València, Valencia, Spain.
National Research University Higher School of Economics · RUUniversitat Politècnica de València · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Digital tracesMental health predictionSubjective well-being

Identifiers

PMID35402139
PMCPMC8978494
OpenAlexW4225523462

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.