E-cigarettes are becoming increasingly popular, but data on the potential risks of long-term use are sparse. This cross-sectional study conducted in the United Kingdom examines the potential risks ... Background: Given the rapid increase in the popularity of e-cigarettes and the paucity of associated longitudinal health-related data, the need to assess the potential risks of long-term use is ess...

Background and Aims Electronic cigarettes (e‐cigarettes) are rapidly increasing in popularity. Two randomized controlled trials have suggested that e‐cigarettes can aid smoking cessation, but there are many factors that could influence their real‐world effectiveness. This study aimed to assess, using an established methodology, the effectiveness of e‐cigarettes when used to aid smoking cessation compared with nicotine replacement therapy (NRT) bought over‐the‐counter and with unaided quitting in the general population. Design and Setting A large cross‐sectional survey of a representative sample of the English population. Participants The study included 5863 adults who had smoked within the previous 12 months and made at least one quit attempt during that period with either an e‐cigarette only (n = 464), NRT bought over‐the‐counter only (n = 1922) or no aid in their most recent quit attempt (n = 3477). Measurements The primary outcome was self‐reported abstinence up to the time of the survey, adjusted for key potential confounders including nicotine dependence. Findings E‐cigarette users were more likely to report abstinence than either those who used NRT bought over‐the‐counter [odds ratio (OR) = 2.23, 95% confidence interval (CI) = 1.70–2.93, 20.0 versus 10.1%] or no aid (OR = 1.38, 95% CI = 1.08–1.76, 20.0 versus 15.4%). The adjusted odds of non‐smoking in users of e‐cigarettes were 1.63 (95% CI = 1.17–2.27) times higher compared with users of NRT bought over‐the‐counter and 1.61 (95% CI = 1.19–2.18) times higher compared with those using no aid. Conclusions Among smokers who have attempted to stop without professional support, those who use e‐cigarettes are more likely to report continued abstinence than those who used a licensed NRT product bought over‐the‐counter or no aid to cessation. This difference persists after adjusting for a range of smoker characteristics such as nicotine dependence.

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Background: The public health impact of health and well-being digital interventions is dependent upon sufficient real-world uptake and engagement. Uptake is currently largely dependent on popularity indicators (eg, ranking and user ratings on app stores), which may not correspond with effectiveness, and rapid disengagement is common. Therefore, there is an urgent need to identify factors that influence uptake and engagement with health and well-being apps to inform new approaches that promote the effective use of such tools. Objective: This review aimed to understand what is known about influences on the uptake of and engagement with health and well-being smartphone apps among adults. Methods: We conducted a systematic review of quantitative, qualitative, and mixed methods studies. Studies conducted on adults were included if they focused on health and well-being smartphone apps reporting on uptake and engagement behavior. Studies identified through a systematic search in Medical Literature Analysis and Retrieval System Online, or MEDLARS Online (MEDLINE), EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsychINFO, Scopus, Cochrane library databases, DataBase systems and Logic Programming (DBLP), and Association for Computing Machinery (ACM) Digital library were screened, with a proportion screened independently by 2 authors. Data synthesis and interpretation were undertaken using a deductive iterative process. External validity checking was undertaken by an independent researcher. A narrative synthesis of the findings was structured around the components of the capability, opportunity, motivation, behavior change model and the theoretical domains framework (TDF). Results: Of the 7640 identified studies, 41 were included in the review. Factors related to uptake (U), engagement (E), or both (B) were identified. Under capability, the main factors identified were app literacy skills (B), app awareness (U), available user guidance (B), health information (E), statistical information on progress (E), well-designed reminders (E), features to reduce cognitive load (E), and self-monitoring features (E). Availability at low cost (U), positive tone, and personalization (E) were identified as physical opportunity factors, whereas recommendations for health and well-being apps (U), embedded health professional support (E), and social networking (E) possibilities were social opportunity factors. Finally, the motivation factors included positive feedback (E), available rewards (E), goal setting (E), and the perceived utility of the app (E). Conclusions: Across a wide range of populations and behaviors, 26 factors relating to capability, opportunity, and motivation appear to influence the uptake of and engagement with health and well-being smartphone apps. Our recommendations may help app developers, health app portal developers, and policy makers in the optimization of health and well-being apps.

Aims: To estimate the association of smoking status with rates of (i) infection, (ii) hospitalization, (iii) disease severity and (iv) mortality from SARS‐CoV‐2/COVID‐19 disease.Design: Living rapid review of observational and experimental studies with random‐effects hierarchical Bayesian meta‐analyses. Published articles and pre‐prints were identified via MEDLINE and medRxiv.Setting: Community or hospital, no restrictions on location.Participants: Adults who received a SARS‐CoV‐2 test or a COVID‐19 diagnosis.Measurements: Outcomes were SARS‐CoV‐2 infection, hospitalization, disease severity and mortality stratified by smoking status. Study quality was assessed (i.e. ‘good’, ‘fair’ and ‘poor’).Findings: Version 7 (searches up to 25 August 2020) included 233 studies with 32 ‘good’ and ‘fair’ quality studies included in meta‐analyses. Fifty‐seven studies (24.5%) reported current, former and never smoking status. Recorded smoking prevalence among people with COVID‐19 was generally lower than national prevalence. Current compared with never smokers were at reduced risk of SARS‐CoV‐2 infection [relative risk (RR) = 0.74, 95% credible interval (CrI) = 0.58–0.93, τ = 0.41]. Data for former smokers were inconclusive (RR = 1.05, 95% CrI = 0.95–1.17, τ = 0.17), but favoured there being no important association (21% probability of RR ≥ 1.1). Former compared with never smokers were at somewhat increased risk of hospitalization (RR = 1.20, CrI = 1.03–1.44, τ = 0.17), greater disease severity (RR = 1.52, CrI = 1.13–2.07, τ = 0.29) and mortality (RR = 1.39, 95% CrI = 1.09–1.87, τ = 0.27). Data for current smokers were inconclusive (RR = 1.06, CrI = 0.82–1.35, τ = 0.27; RR = 1.25, CrI = 0.85–1.93, τ = 0.34; RR = 1.22, 95% CrI = 0.78–1.94, τ = 0.49, respectively), but favoured there being no important associations with hospitalization and mortality (35% and 70% probability of RR ≥ 1.1, respectively) and a small but important association with disease severity (79% probability of RR ≥ 1.1).Conclusions: Compared with never smokers, current smokers appear to be at reduced risk of SARS‐CoV‐2 infection, while former smokers appear to be at increased risk of hospitalization, increased disease severity and mortality from COVID‐19. However, it is uncertain whether these associations are causal.