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Dynamic modelling of chronotype and hypo/manic and depressive symptoms in young people with emerging mental disorders.

Timothy R WongIan B HickieJoanne S CarpenterElizabeth M ScottAdam John GuastellaParisa VidafarJan ScottDaniel Francis HermensJacob J Crouse
Published in: Chronobiology international (2023)
There is significant interest in the possible influence of chronotype on clinical states in young people with emerging mental disorders. We apply a dynamic approach (bivariate latent change score modelling) to examine the possible prospective influence of chronotype on depressive and hypo/manic symptoms in a youth cohort with predominantly depressive, bipolar, and psychotic disorders ( N  = 118; 14-30-years), who completed a baseline and follow-up assessment of these constructs (mean interval = 1.8-years). Our primary hypotheses were that greater baseline eveningness would predict increases in depressive but not hypo/manic symptoms. We found moderate to strong autoregressive effects for chronotype (β = -0.447 to -0.448, p  < 0.001), depressive (β = -0.650, p  < 0.001) and hypo/manic symptoms (β = -0.819, p  < 0.001). Against our predictions, baseline chronotypes did not predict change in depressive (β = -0.016, p  = 0.810) or hypo/manic symptoms (β = 0.077, p  = 0.104). Similarly, the change in chronotype did not correlate with the change in depressive symptoms (β = -0.096, p  = 0.295) nor did the change in chronotype and the change in hypo/manic symptoms (β = -0.166, p  = 0.070). These data suggest that chronotypes may have low utility for predicting future hypo/manic and depressive symptoms in the short term, or that more frequent assessments over longer periods are needed to observe these associations. Future studies should test whether other circadian phenotypes (e.g. sleep-wake variability) are better indicators of illness course.
Keyphrases
  • bipolar disorder
  • depressive symptoms
  • sleep quality
  • social support
  • physical activity
  • young adults
  • high intensity
  • middle aged
  • machine learning