Can linguistic analysis be used to identify whether adolescents with a chronic illness are depressed?. (9th January 2020) - Jones, Lauren Stephanie; Anderson, Emma; Loades, Maria; Barnes, Rebecca; Crawley, Esther
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 (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.