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Impairments of facial detection in tobacco use disorder: baseline data and impact of smoking duration

  • Gabriella M. Silva
  • , Jandirlly J. Souto
  • , Thiago P. Fernandes
  • , Thiago A. Bonifacio
  • , Natalia L. Almeida*
  • , Giulliana H. Gomes
  • , Fatima M. Felisberti
  • , Natanael A. Santos
  • *Corresponding author for this work

    Research output: Contribution to journalArticle (Academic Journal)peer-review

    12 Citations (Scopus)

    Abstract

    Objective: Chronic tobacco consumption, classified as tobacco use disorder (TUD), has been associated with a variety of health problems. Investigations of face processing in TUD are hampered by lack of evidence. Here, we evaluated facial detection in TUD and assessed test-retest reliability for a facial detection task.

    Methods: Participants were instructed to detect the orientation (either left or right) of a face when it was presented with a face/non-face pair on the monitor screen, using Bayesian entropy estimation. Bland-Altman analysis and intraclass correlation coefficients were used to test the reliability of the task. The general linear model and Bayesian statistics were then used to evaluate differences between TUD (n=48) and healthy controls (n=34).

    Results: The reliability of the task was high for the 96 stimuli presentations. Slower reaction times (p < 0.001) and lower discrimination index (p < 0.001) were observed in the TUD group than for healthy controls. Mediation analysis indicated direct effects of smoking duration on reaction time (p < 0.001) and discrimination index (p < 0.001).

    Conclusions: Overall, we observed high reliability of this task and reduction of facial detection in tobacco use disorder. We conclude our findings are significant for public health initiatives and call for follow-up studies.
    Original languageEnglish
    Pages (from-to)376-384
    Number of pages9
    JournalBrazilian Journal of Psychiatry
    Volume43
    Issue number4
    DOIs
    Publication statusPublished - 28 Sept 2020

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

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