Linguistic distancing predicts response to a digital single-session intervention for adolescent depression

Area of Science:

• Psychology
• Linguistics
• Digital Intervention

Background:

• Youth mental health is a significant concern.
• Early detection of struggling youth is crucial.
• Digital interventions offer scalable solutions for mental health support.

Purpose of the Study:

• To investigate linguistic patterns in adolescent writing.
• To examine the relationship between linguistic characteristics and mental health outcomes.
• To identify linguistic predictors of treatment success in a digital intervention.

Main Methods:

• Analysis of adolescent writing samples (n=638) from a digital growth mindset intervention trial.
• Utilized Linguistic Inquiry and Word Count (LIWC) for linguistic analysis.
• Employed correlation analyses and Least Absolute Shrinkage and Selection Operator (LASSO) regression models.

Main Results:

• Increased linguistic distancing correlated with reduced baseline hopelessness and higher perceived agency.
• Linguistic distancing significantly predicted greater reductions in depressive symptoms over three months.
• Linguistic distancing accounted for 27% of the variance in depression trajectories, controlling for baseline depression.

Conclusions:

• Linguistic distancing in adolescent writing is a promising indicator for detecting youth at risk.
• This linguistic feature can predict treatment outcomes in digital mental health interventions.
• Further research into language-based markers for youth mental health is warranted.