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Subjective quality of life factorial structure across mental disorders: should we switch to assessing dimensions?

Eleni PetkariStefan Priebe
Published in: European archives of psychiatry and clinical neuroscience (2022)
A two-factor structure of subjective quality of life (SQoL) was established for patients with schizophrenia with the dimensions being 'Life and Health' and 'Living Environment'. This study investigated whether the same structure applies in patients with mood and anxiety disorders and, if so, whether the dimension scores differ between the three diagnostic groups. SQoL data of 1366 patients with mood and 419 patients with anxiety disorders obtained on the Manchester Short Assessment of Quality of Life (MANSA) were retrieved from 3 multisite studies. We performed Confirmatory Factor Analyses (CFAs) based on the MANSA SQoL items of each diagnostic sample. Next, through a series of Kruskal-Wallis and Mann-Whitney tests, we compared the scores of the two factors across patients with mood disorders, anxiety disorders and schizophrenia. The two CFAs showed adequate fit of the two-factor structure across mood and anxiety disorders. The dimension scores on 'Life and Health' differed significantly between all three diagnostic groups. They were lowest in patients with anxiety disorders, higher in patients with mood disorders and highest in patients with schizophrenia. However, on the 'Living Environment' dimension, patients with mood disorders had significantly higher scores than patients with schizophrenia, whilst patients with anxiety disorders did not differ significantly from either other group. The findings suggest that a two-factor structure of SQoL applies across mental disorders. The two dimensions vary across diagnostic groups in different ways. Assessing two dimensions of SQoL may provide more specific and relevant information than global scores.
Keyphrases
  • bipolar disorder
  • sleep quality
  • healthcare
  • public health
  • mental health
  • physical activity
  • electronic health record
  • artificial intelligence
  • human health
  • big data
  • case control
  • data analysis