Predictive modelling of stress, anxiety and depression: A network analysis and machine learning study

OBJECTIVE: This study assessed predictors of stress, anxiety and depression during the COVID-19 pandemic using a large number of demographic, COVID-19 context and psychological variables. METHODS: Data from 741 adults were drawn from the Boston College daily sleep and well-being survey. Baseline demographics, the long version of the daily surveys and the round one assessment of the survey were utilized for the present study. A Gaussian graphical model (GGM) was estimated as a feature selection technique on a subset of ordinal/continuous variables. An ensemble Random Forest (RF) machine learning algorithm was used for prediction. RESULTS: GGM was found to be an efficient feature selection method and supported the findings derived from the RF machine learning model. Psychological variables were significant predictors of stress, anxiety and depression, while demographic and COVID-19-related factors had minimal predictive value. The outcome variables were mutually predictive of each other, and negative affect and subjective sleep quality were the common predictors of these outcomes of stress, anxiety, and depression. CONCLUSION: The study identifies risk factors for adverse mental health outcomes during the pandemic and informs interventions to mitigate the impact on mental health.