• Eilidh M Smith, Shirley Reynolds, Faith Orchard, Heather C Whalley, and Stella Wy Chan.
• Department of Clinical Psychology, University of Edinburgh, Doorway 6 Medical Quad, Teviot Place, Edinburgh EH8 9AG, United Kingdom. Electronic address: eilidh.smith@ed.ac.uk.
• J Affect Disord. 2018 Dec 1; 241: 446-453.

BackgroundAdolescence represents a period of vulnerability to affective disorders. Neuroticism is considered a heritable risk factor for depression, but is not directly amenable to intervention. Therefore, it is important to identify the contributions of modifiable risk factors. Negative cognitive biases are implicated in the onset and maintenance of affective disorders in adults, and may represent modifiable risk factors in adolescence.Aim(S)This study sought to assess to what extent cognitive biases are able to predict depression, anxiety and wellbeing beyond that of neuroticism in adolescents.MethodsAdolescents (N = 99), recruited from Scottish secondary schools (54.5% female; mean age = 14.7), ensured a sample representing the breadth of the mental health spectrum. In line with prevalence estimates, 18% of this sample demonstrated clinical levels of depression symptoms. Cognitive biases of autobiographical memory, self-referential memory, ambiguous scenarios interpretation, facial expression recognition, rumination and dysfunctional attitudes were assessed. Depression, anxiety, and wellbeing were indexed using the Mood and Feelings Questionnaire, Spence Children's Anxiety Scale and the BBC Subjective Wellbeing Scale.ResultsRegression analyses demonstrated neuroticism to significantly predict depression, anxiety and wellbeing. The addition of cognitive biases resulted in a significant increase of explained variance with final models explaining just over 50% of variances of depression, anxiety and wellbeing.ConclusionThese findings demonstrate that cognitive biases explain mental health symptoms over and above that of neuroticism. Depressive symptomology was particularly related to self-referential memory bias, while anxiety was predicted by interpretive bias. The key clinical implication is that targeting specific biases based on diagnostic features may be of particular benefit in alleviating distress and promoting wellbeing.Copyright © 2018. Published by Elsevier B.V.

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