Can linguistic analysis be used to identify whether adolescents with a chronic illness are depressed?

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 (Fatigue in teenagers on the interNET in the National Health Service) treatment in a national randomized controlled trial were examined. A theoretically driven linguistic coding scheme was developed and used to categorize comorbid depression in e-consultations using computerized content analysis. Linguistic coding scheme categorization was subsequently compared with classification of depression using the Revised Children's Anxiety and Depression Scale 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 categorized three (19%) of our sample consistently with self-report assessment. Of all 12 identified linguistic features, differences in language use by categorization 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.