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Probing different aspects of short and ill-timed sleep in adolescents using the Morningness-Eveningness Scale for Children.

Sareh PanjehSabine PompeiaHugo Cogo-Moreira
Published in: Chronobiology international (2021)
Sleep problems among adolescents are believed to be related to the circadian changes that occur at this age. Therefore, most self-report instruments that measure sleep patterns in adolescence focus solely on measuring circadian rhythms. However, sleep-wake cycles reflect both circadian and homeostatic processes. Recently, it was shown that answers to the Morningness-Eveningness Questionnaire for adults, which is used to assess circadian typology, were able to identify three interrelated latent factors: two that can be conceptualized as homeostatic (sensitivity to the build-up of sleep pressure and efficiency of dissipation of sleep pressure) and a less well-defined factor related to activity preference time (APT). To better understand self-reported changes in sleep patterns in adolescents we applied confirmatory factor analysis to explore whether responses to the Morningness-Eveningness Scale for Children (MESC) could also identify these three factors. The sample comprised 397, 9- to 17-year-olds. A three-correlated and a bifactor-(S-1) model (with sleep onset characteristics as a reference factor) had acceptable/good fit indices. This indicates that the MESC captures dissociable, but interrelated, homeostatic and circadian processes in addition to APT. These factors correlated with corresponding reported sleep habits, showing individual differences that may be more associated with sleep difficulties than the effects of age, which only correlated very modestly with some sleep habits. Our results indicate that the MESC can show distinct individual differences in three sleep factors that can help identify adolescents at higher risk of sleep-related problems that may require factor-specific interventions.
Keyphrases
  • physical activity
  • sleep quality
  • young adults
  • mental health
  • depressive symptoms
  • cross sectional
  • data analysis