JMIR Publications

ABSTRACT

Background:

Australians living in rural and remote areas are at elevated risk of mental health problems, and must overcome barriers to help-seeking, such as poor access, stigma and entrenched stoicism. E-mental health services circumvent such barriers using technology, and text-based services are particularly well suited to clients concerned with privacy and self-presentation. They allow the client to reflect on the therapy session after it has ended as the transcript is stored on their device. The text transcript also offers researchers an opportunity to analyse language use patterns and explore how these relate to mental health status.

Objective:

In this project, we investigated whether computational linguistic techniques can be applied to text-based communications with the goal of identifying a client’s mental health status.

Methods:

Client-therapist text message transcripts were analysed using the Linguistic Inquiry and Word Count tool. We examined whether the resulting word counts related to the participants’ presenting problems or their self-ratings of mental health at the conclusion of counselling.

Results:

The results confirmed that word use patterns could be used to differentiate whether a client had one of the top three presenting problems (depression, anxiety, or stress), as well as prospectively to predict their self-rated mental health after counselling had concluded.

Conclusions:

These findings suggest that language use patterns are useful both for researchers and for clinicians trying to identify individuals at risk of mental health problems, with potential applications in screening and targeted intervention.