Using natural language processing to identify patterns associated with depression, anxiety, and stress symptoms during the COVID-19 pandemic.

Background: Combining data-driven natural language processing techniques with traditional methods using predefined word lists may offer greater insights into the connections between language patterns and depression and anxiety symptoms, particularly within specific stressful contexts.

Methods: Between 2020 and 2021, 1106 participants wrote narrative responses describing their experiences during the COVID-19 pandemic and completed the Depression Anxiety Stress Scale-21 (DASS). We investigated language patterns associated with DASS symptoms using established categories from Linguistic Inquiry and Word Count (LIWC) and sentiment analysis, as well as exploratory natural language processing techniques.

Cortisol reactivity to a laboratory stressor predicts increases in depressive symptoms in perinatal and nulliparous women during population-level stress.

Background: Research suggests a link between stress and depression, especially in high-risk groups. The perinatal period is known as a time of increased risk for depression and pregnancy has been associated with alterations in cortisol levels; however, limited research has assessed cortisol reactivity during pregnancy. Finally, no studies have yet examined whether cortisol reactivity predicts later depressive symptoms during a population-level stressor, such as the COVID-19 pandemic.