Background: Between 2018 and 2020, the US Food and Drug Administration announced various restrictions on e-cigarette manufacturers in response to a perceived epidemic of e-cigarette use and nicotine dependence among high school students. The stimulus came from headline figures from the 2018 and 2019 National Youth Tobacco Survey (NYTS). We analysed e-cigarette use and dependence in the NYTS in relation to lifetime history of use of tobacco products.Design and setting: Nationally representative annual survey of high school students 2017 to 2019.Participants: 10,186 students in 2017, 10,991 in 2018 and 10,097 in 2019.Measurements: Any use of e-cigarettes in past 30 days, frequent e-cigarette use (≥20 of past 30 days) and indicators of tobacco or nicotine dependence (strong craving in past 30 days; wanting to use within 30 minutes of waking) were analysed in relation to lifetime tobacco product use history, ranging from never use through to lifetime smoking of >100 cigarettes.Findings: Past-30-day e-cigarette use increased from 11.7% in 2017 to 20.8% in 2018, and 27.5% in 2019. In 2019 it was reported in 13.3% of those who had never tried any other tobacco product, 30.9% of those who had tried only a non-combustible product (OR 2.9, CI 1.9-4.5), and in 73.8 % of those who had smoked more than 100 cigarettes in their lifetime (OR 18.3, CI 8.4-40.1) Frequent use occurred in 1.0% of otherwise tobacco naive users in 2018 and 2.1% in 2019. Among otherwise tobacco naive past-30-day e-cigarette users in 2019, 8.7% reported craving and 2.9% reported wanting to use within 30 minutes of waking.Conclusions: While use of e-cigarettes in US high-school students increased sharply between 2017 and 2019, frequent use and signs of e-cigarette dependence remained rare in students who had only ever used e-cigarettes and never any other tobacco product.

Background: Face masks have been proposed as an important way of reducing transmission of viral respiratory infections, including SARS-CoV-2. Objective: To assess the likelihood that wearing face masks in community settings reduces transmission of viral respiratory infections. Methods: We conducted a rapid evidence review and used a Bayesian statistical approach to analysing experimental and observational studies conducted in community-dwelling children and adults that assessed the effectiveness of face mask wearing (vs. no face masks) on self-reported, laboratory-confirmed, or clinically diagnosed viral respiratory infections. Results: Eleven RCTs and 10 observational studies met the inclusion criteria. The calculation of Bayes factors and cumulative posterior odds from the RCTs showed a moderate likelihood of a small effect of wearing surgical face masks in community settings in reducing self-reported influenza-like illness (ILI) (cumulative posterior odds = 3.61). However, the risk of reporting bias was high and evidence of reduction of clinically- or laboratory-confirmed infection was equivocal (cumulative posterior odds = 1.07 and 1.22, respectively). Observational studies yielded evidence of a negative association between face mask wearing and ILI but with high risk of confounding and reporting bias. Conclusions: Available evidence from RCTs is equivocal as to whether or not wearing face masks in community settings results in a reduction in clinically- or laboratory-confirmed viral respiratory infections. No relevant studies concerned SARS-CoV-2 or were undertaken in community settings in the UK.

Objective: To assess the possible effectiveness of an online version of the Risk Acceptance Ladder (RAL) in promoting help-seeking relating to a range of health behaviours. Design: Pilot randomised controlled trial. Methods: 843 UK adults were recruited, of whom 602 engaged in at least one of four risky health behaviours; cigarette smoking, excessive alcohol consumption, insufficient physical activity, or low fruit and vegetable consumption. Those with no immediate plans to change completed a behaviour specific RAL (n = 171). Participants were randomised to one of two conditions; a short message congruent (on-target, n = 73) or incongruent (off-target, n = 98) with their RAL response. Performance of the RAL was assessed by participants’ ability to select an applicable RAL item and reported ease of use of the RAL. Effectiveness of the RAL was assessed by whether or not participants clicked a link to receive information about changing their target behaviour. Results: Two thirds (68.9%, 95% CI = 61.8%-75.3%) of participants were able to select an applicable RAL item that corresponded to what they believed would need to change in order to change their target behaviour and 64.9% (95% CI = 57.5%-71.7%) reported that it was easy to select one option. Participants allocated to the on-target group were more likely to click on the link to receive information (31.5% vs 19.4%; OR = 2.07, 95% CI = 1.01-4.26). Conclusions: The Risk Acceptance Ladder may have utility as a tool for tailoring messages to prompt initial steps to engaging in self-protective behaviours.

The COM-B model of behaviour is widely used to identify what needs to change in order for a behaviour change intervention to be effective. It identifies three factors that need to be present for any behaviour to occur: capability, opportunity and motivation. These factors interact over time so that behaviour can be seen as part of a dynamic system with positive and negative feedback loops. Motivation is a core part of the model and the PRIME Theory of motivation provides a framework for understanding how reflective thought processes (Planning and Evaluation processes) and emotional and habitual processes (Motive and Impulse/inhibition processes) interact at every moment leading to behaviour (Responses) at that moment.