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Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning-Based
Jihan Ryu1, Emese Sükei2, Agnes Norbury1
1Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
JMIR Mental Health
|September 15, 2021
Summary
Patients with severe anxiety used communication apps less and social networking apps more during the COVID-19 lockdown. This digital social behavior shift can help identify individuals at risk for psychiatric issues.
Area of Science:
- Psychiatry and Digital Health
Background:
- Anxiety symptoms during public health crises correlate with poor mental health outcomes and decision-making.
- The interplay between social media use and clinical anxiety during pandemics remains understudied.
Purpose of the Study:
- To examine social media usage patterns (communication and social networking apps) in psychiatric patients during the COVID-19 surge in Madrid.
- To assess the short-term anxiety symptoms in these patients using the 7-item General Anxiety Disorder scale.
Main Methods:
- Repeated measures analysis of variance was used to analyze shifts in social media usage from pre-lockdown to post-lockdown.
- A machine learning model (hidden Markov model and logistic regression) predicted clinical anxiety based on app usage and clinical variables.
Main Results:
- Patients with clinical anxiety showed a smaller increase in communication app usage during lockdown compared to non-clinical groups (P=.05).
- The machine learning model achieved 62.30% accuracy and an area under the receiver operating curve of 0.70 in identifying clinical anxiety.
Conclusions:
- Severe anxiety patients exhibited reduced communication app activity and increased social networking app engagement during lockdown, indicating altered digital social behavior for crisis adaptation.
- Predictive modeling using digital biomarkers from social media usage shifts can identify individuals susceptible to psychiatric sequelae.
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