Can linguistic analysis be used to identify whether adolescents with a chronic illness are depressed?, by Lauren Stephanie Jones, Emma Anderson, Maria Loades, Rebecca Barnes, Esther Crawley in Clin Psychol Psychother. 2019 Dec 15. [doi: 10.1002/cpp.2417]

 

Research abstract:

Comorbid depression is common in adolescents with chronic illness. We aimed to design and test a linguistic coding scheme for identifying depression in adolescents with Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME), by exploring features of e-consultations within online cognitive behavioural therapy treatment.

E-consultations of 16 adolescents (aged 11 – 17) receiving FITNET-NHS treatment in a national randomised controlled trial were examined. A theoretically-driven linguistic coding scheme was developed and used to categorise comorbid depression in e-consultations using computerised content analysis.

Linguistic coding scheme categorisation was subsequently compared to classification of depression using the Revised Children’s Anxiety and Depression Scale (RCADS) published cut-offs (t-scores ≥ 65, ≥ 70). Extra linguistic elements identified deductively and inductively were compared with self-reported depressive symptoms after unblinding.

The linguistic coding scheme categorised three (19%) of our sample consistently with self-report assessment. Of all 12 identified linguistic features, differences in language use by categorisation of self-report assessment were found for ‘past-focus’ words (mean rank frequencies: 1.50 for no depression, 5.50 for possible depression, and 10.70 for probable depression; p < .05) and ‘discrepancy’ words (mean rank frequencies: 16.00 for no depression, 11.20 for possible depression, and 6.40 for probable depression; p < .05).

The linguistic coding profile developed as a potential tool to support clinicians in identifying comorbid depression in e-consultations showed poor value in this sample of adolescents with CFS/ME. Some promising linguistic features were identified, warranting further research with larger samples.

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