Machine learning approach for anxiety and sleep disorders analysis during COVID-19 lockdown
L Jani Anbarasi1, Malathy Jawahar2, Vinayakumar Ravi3
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, 600 127 India.
Health and Technology
|June 7, 2022
Summary
The COVID-19 pandemic significantly increased anxiety and sleep disorders. Students and those with lower social status were particularly vulnerable to mental health issues, with specific academic and workload stressors identified.
Area of Science:
- Psychiatry
- Public Health
- Epidemiology
Background:
- The COVID-19 pandemic induced widespread anxiety and stress.
- Sleep disturbances and mental health issues became prevalent across diverse populations.
- Understanding these impacts is crucial for public health interventions.
Purpose of the Study:
- To analyze sleep and anxiety disorders in relation to the COVID-19 pandemic.
- To investigate symptoms, grievances, and contributing factors.
- To explore relationships between pandemic stress and mental health outcomes.
Main Methods:
- Survey of 740 participants (51.3% male, 48.7% female).
- Utilized Pittsburgh Sleep Quality Index (PSQI) and General Anxiety Disorder (GAD-7) scales.
- Employed statistical analysis and a Random Forest model for student data.
Main Results:
- A positive correlation was found between PSQI and GAD-7 scores.
- Occupation affected anxiety (GAD-7) but not sleep quality (PSQI) after adjusting for age and gender.
- Students and individuals of lower social status exhibited higher susceptibility to mental health disturbances.
Conclusions:
- The COVID-19 pandemic exacerbated sleep and anxiety disorders.
- Students face unique stressors including online learning challenges and workload.
- Lower social status is linked to increased mental health issues, necessitating targeted support.
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