Shift in social media app usage during covid-19 lockdown and clinical anxiety symptoms: Machine learning-based ecological momentary assessment study

e-Archivo Repository

Show simple item record Ryu, Jihan Sükei, Emese Norbury, Agnes Liu, Shelley H Campaña Montes, Juan José Baca García, Enrique Artés Rodríguez, Antonio Perez-Rodriguez, M. Mercedes 2022-03-18T11:00:40Z 2022-03-18T11:00:40Z 2021-09-15
dc.identifier.bibliographicCitation Ryu, J., Sükei, E., Norbury, A., H Liu, S., Campaña-Montes, J. J., Baca-Garcia, E., Artés, A., & Perez-Rodriguez, M. M. (2021). Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning–Based Ecological Momentary Assessment Study. JMIR Mental Health, 8(9), e30833.
dc.identifier.issn 2368-7959
dc.description.abstract Background: Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored. Objective: We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up. Methods: The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning–based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status. Results: Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F1,72=3.84, P=.05). The machine learning model achieved a mean accuracy of 62.30% (SD 16%) and area under the receiver operating curve 0.70 (SD 0.19) in 10-fold cross-validation in identifying the clinical anxiety group. Conclusions: Patients who reported severe anxiety symptoms were less active in communication apps after the mandated lockdown and more engaged in social networking apps in the overall period, which suggested that there was a different pattern of digital social behavior for adapting to the crisis. Predictive modeling using digital biomarkers—passive-sensing of shifts in category-based social media app usage during the lockdown—can identify individuals at risk for psychiatric sequelae.
dc.description.sponsorship JR was supported by the American Psychiatric Association 2021 Junior Psychiatrist Research Colloquium (NIDA R-13 grant). ES received funding from the European Union Horizon 2020 research and innovation program (Marie Sklodowska-Curie grant 813533). AA is supported by the Spanish Ministerio de Ciencia, Innovación y Universidades (RTI2018-099655-B-I00), the Comunidad de Madrid (Y2018/TCS-4705 PRACTICO-CM), and the BBVA Foundation (Deep-DARWiN grant).
dc.format.extent 13
dc.language.iso eng
dc.publisher JMIR Publications
dc.rights ©Jihan Ryu, Emese Sükei, Agnes Norbury, Shelley H Liu, Juan José Campaña-Montes, Enrique Baca-Garcia, Antonio Artés, M Mercedes Perez-Rodriguez.
dc.rights Atribución 3.0 España
dc.subject.other Anxiety disorder
dc.subject.other Covid-19
dc.subject.other Digital phenotype
dc.subject.other Ecological momentary assessment
dc.subject.other Hidden markov model
dc.subject.other Machine learning
dc.subject.other Public health
dc.subject.other Smartphone
dc.subject.other Social media
dc.title Shift in social media app usage during covid-19 lockdown and clinical anxiety symptoms: Machine learning-based ecological momentary assessment study
dc.type article
dc.subject.eciencia Psicología
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/813533
dc.relation.projectID Gobierno de España. RTI2018-099655-B-I00
dc.relation.projectID Comunidad de Madrid. Y2018/TCS-4705 PRACTICO-CM
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 9
dc.identifier.publicationlastpage 13
dc.identifier.publicationtitle JMIR Mental Health
dc.identifier.publicationvolume 8
dc.identifier.uxxi AR/0000028889
dc.contributor.funder Comunidad de Madrid
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Ciencia, Innovación y Universidades (España)
dc.affiliation.dpto UC3M. Departamento de Teoría de la Señal y Comunicaciones
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Tratamiento de la Señal y Aprendizaje (GTSA)
 Find Full text

Files in this item

*Click on file's image for preview. (Embargoed files's preview is not supported)

The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record