ObjectiveThe aim of this review was to investigate whether e-cigarette use compared with non-use in young non-smokers is associated with subsequent cigarette smoking.Data sourcesPubMed, Embase, Web of Science, Wiley Cochrane Library databases, and the 2018 Society for Research on Nicotine and Tobacco and Society for Behavioural Medicine conference abstracts.Study selectionAll studies of young people (up to age 30 years) with a measure of e-cigarette use prior to smoking and an outcome measure of smoking where an OR could be calculated were included (excluding reviews and animal studies).Data extractionIndependent extraction was completed by multiple authors using a preprepared extraction form.Data synthesisOf 9199 results, 17 studies were included in the meta-analysis. There was strong evidence for an association between e-cigarette use among non-smokers and later smoking (OR: 4.59, 95% CI: 3.60 to 5.85) when the results were meta-analysed in a random-effects model. However, there was high heterogeneity (I2=88%).ConclusionsAlthough the association between e-cigarette use among non-smokers and subsequent smoking appears strong, the available evidence is limited by the reliance on self-report measures of smoking history without biochemical verification. None of the studies included negative controls which would provide stronger evidence for whether the association may be causal. Much of the evidence also failed to consider the nicotine content of e-liquids used by non-smokers meaning it is difficult to make conclusions about whether nicotine is the mechanism driving this association.

Objective: The aim of this review was to investigate whether e-cigarette use compared to non-use in young non-smokers is associated with subsequent cigarette smoking. Data sources: PubMed, Embase, Web of Science, Wiley Cochrane Library databases, and the 2018 Society for Research on Nicotine and Tobacco and Society for Behavioural Medicine conference abstracts. Study selection: All studies of young people (up to age 30 years) with a measure of e-cigarette use prior to smoking and an outcome measure of smoking where an odds ratio could be calculated were included (excluding reviews and animal studies). Data Extraction: Independent extraction was completed by multiple authors using a pre-prepared extraction form. Data synthesis: Of 9,199 results, 17 studies were included in the meta-analysis. There was strong evidence for an association between e-cigarette use among non-smokers and later smoking (OR 4.59, 95% CI 3.60 to 5.85) when the results were meta-analysed in a random effects model. However, there was high heterogeneity (I2 = 88%). Conclusions: Whilst the association between e-cigarette use among non-smokers and subsequent smoking appears strong, the available evidence is limited by the reliance on self-report measures of smoking history without biochemical verification. None of the studies included negative controls which would provide stronger evidence for whether the association may be causal. Much of the evidence also failed to consider the nicotine content of e-liquids used by non-smokers meaning it is difficult to make conclusions about whether nicotine is the mechanism driving this association.

Cognitive bias modification is a potential low-intensity intervention for mood disorders, but previous studies have shown mixed success. This study explored whether facial interpretation bias modification (FIBM), a similar paradigm designed to shift emotional interpretation (and/or perception) of faces would transfer to: (i) self-reported symptoms and (ii) a battery of cognitive tasks. In a preregistered, double-blind randomized controlled trial, healthy participants received eight online sessions of FIBM (N = 52) or eight sham sessions (N = 52). While we replicate that FIBM successfully shifts ambiguous facial expression interpretation in the intervention group, this failed to transfer to the majority of self-report or cognitive measures. There was, however, weak, inconclusive evidence of transfer to a self-report measure of stress, a cognitive measure of anhedonia, and evidence that results were moderated by trait anxiety (whereby transference was greatest in those with higher baseline symptoms). We discuss the need for work in both larger and clinical samples, while urging caution that these FIBM training effects may not transfer to clinically relevant domains.

Studying the effects of experimentally induced anxiety in healthy volunteers may increase our understanding of the mechanisms underpinning anxiety disorders. Experimentally induced stress (via threat of unpredictable shock) improves accuracy at withholding a response on the sustained attention to response task (SART), and in separate studies improves accuracy to classify fearful faces, creating an affective bias. Integrating these findings, participants at two public science engagement events (n = 46, n = 55) were recruited to explore the effects of experimentally induced stress on an affective version of the SART. We hypothesized that we would see an improved accuracy at withholding a response to affectively congruent stimuli (i.e. increased accuracy at withholding a response to fearful ‘no-go’ distractors) under threat of shock. Induced anxiety slowed reaction time, and at the second event quicker responses were made to fearful stimuli. However, we did not observe improved inhibition overall during induced anxiety, and there was no evidence to suggest an interaction between induced anxiety and stimulus valence on response accuracy. Indeed Bayesian analysis provided decisive evidence against this hypothesis. We suggest that the presence of emotional stimuli might make the safe condition more anxiogenic, reducing the differential between conditions and knocking out any threat-potentiated improvement.

Cognitive bias modification (CBM) is a potential low-intensity intervention for mood disorders, but previous studies have shown mixed success. The current study explored whether CBM designed to shift emotional perception of faces would transfer to: a) a battery of cognitive tasks, and b) self-reported symptoms. In a preregistered, double-blind randomised controlled trial, healthy participants received eight online sessions of CBM (N=52) or eight sham sessions (N=52). While we replicate that CBM successfully shifts ambiguous facial expression interpretation in the intervention group, this failed to transfer to the majority of cognitive or self-report measures. There was, however, weak, inconclusive evidence of transfer to a self-report measure of stress, a cognitive measure of anhedonia, and a ceiling effect (whereby transference was greatest in those with higher symptoms). We discuss the need for work in both larger and clinical samples, whilst urging caution that these CBM training effects may not transfer to clinically relevant domains.