Emma Beard

Real‐world effectiveness of e‐cigarettes when used to aid smoking cessation: a cross‐sectional population study

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.

Prevalence and characteristics of e-cigarette users in Great Britain: Findings from a general population survey of smokers

Background E-cigarettes may be effective smoking cessation aids and their use by smokers has been growing rapidly. It is important to observe and assess natural patterns in the use of e-cigarettes whilst experimental data accumulates. This paper reports the prevalence of e-cigarette awareness, beliefs and usage, including brand choice, and characterises the socio-demographic and smoking profile associated with current use, among the general population of smokers and recent ex-smokers. Methods Data were obtained from 3538 current and 579 recent ex-smokers in a cross-sectional online survey of a national sample of smokers in Great Britain in November and December 2012. Differences between current and recent ex-smokers in the prevalence of e-cigarette awareness, beliefs and usage were examined and the socio-demographic and smoking profile associated with current use of e-cigarettes was assessed in a series of simple and multiple logistic regressions. Results Ninety-three percent of current and recent ex-smokers (n = 3841) were aware of e-cigarettes. Approximately a fifth (n = 884) were currently using e-cigarettes, whilst just over a third (n = 1507) had ever used them. Sixty-seven percent of the sample (n = 2758) believed e-cigarettes to be less harmful than cigarettes; however, almost a quarter (n = 994) remained unsure. Among both current and recent ex-smokers, the most popular reasons for using were health, cutting down and quitting (each > 80%) and 38% used the brand ‘E-lites’. Among current smokers who were aware of but had never used e-cigarettes, approximately half (n = 1040) were interested in using them in the future. Among current smokers, their use was associated with higher socio-economic status (OR = 1.48, 95%CI = 1.25–1.75), smoking more cigarettes (OR = 1.02, 95%CI = 1.01–1.03) and having a past-year quit attempt (OR = 2.82, 95%CI = 2.38–3.34). Conclusions There is a near universal awareness of e-cigarettes and their use appears to be common among smokers in Great Britain although a quarter of all smokers are unsure as to whether e-cigarettes are less harmful than cigarettes. E-lites – a brand that delivers a low dose of nicotine – is the most popular. E-cigarette users appear to have higher socio-economic status, to smoke more cigarettes per day and to have attempted to quit in the past year.

Association between electronic cigarette use and changes in quit attempts, success of quit attempts, use of smoking cessation pharmacotherapy, and use of stop smoking services in England: time series analysis of population trends.

Objectives To estimate how far changes in the prevalence of electronic cigarette (e-cigarette) use in England have been associated with changes in quit success, quit attempts, and use of licensed medication and behavioural support in quit attempts. Design Time series analysis of population trends. Participants Participants came from the Smoking Toolkit Study, which involves repeated, cross sectional household surveys of individuals aged 16 years and older in England. Data were aggregated on about 1200 smokers quarterly between 2006 and 2015. Monitoring data were also used from the national behavioural support programme; during the study, 8 029 012 quit dates were set with this programme. Setting England. Main outcome measures Prevalence of e-cigarette use in current smokers and during a quit attempt were used to predict quit success. Prevalence of e-cigarette use in current smokers was used to predict rate of quit attempts. Percentage of quit attempts involving e-cigarette use was also used to predict quit attempts involving use of prescription treatments, nicotine replacement therapy (NRT) on prescription and bought over the counter, and use of behavioural support. Analyses involved adjustment for a range of potential confounders. Results The success rate of quit attempts increased by 0.098% (95% confidence interval 0.064 to 0.132; P<0.001) and 0.058% (0.038 to 0.078; P<0.001) for every 1% increase in the prevalence of e-cigarette use by smokers and e-cigarette use during a recent quit attempt, respectively. There was no clear evidence for an association between e-cigarette use and rate of quit attempts (β 0.025; 95% confidence interval −0.035 to 0.085; P=0.41), use of NRT bought over the counter (β 0.006; −0.088 to 0.077; P=0.89), use of prescription treatment (β −0.070; −0.152 to 0.013; P=0.10), or use of behavioural support (β −0.013; −0.102 to 0.077; P=0.78). A negative association was found between e-cigarette use during a recent quit attempt and use of NRT obtained on prescription (β −0.098; −0.189 to −0.007; P=0.04). Conclusion Changes in prevalence of e-cigarette use in England have been positively associated with the success rates of quit attempts. No clear association has been found between e-cigarette use and the rate of quit attempts or the use of other quitting aids, except for NRT obtained on prescription, where the association has been negative. Study registration The analysis plan was preregistered (https://osf.io/fbgj2/).

Use of nicotine replacement therapy for smoking reduction and during enforced temporary abstinence: a national survey of English smokers

AIMS To assess the prevalence of nicotine replacement therapy (NRT) use for smoking reduction (SR) and temporary abstinence (TA), the association between the two and the strength of the association between NRT use for SR or TA and socio-demographic characteristics, cigarette consumption and past quit attempts. DESIGN Cross-sectional monthly surveys. SETTING England. PARTICIPANTS A total of 11, 414 smokers. MEASUREMENTS Participants were asked (i) whether they were reducing the amount they smoked: if so, whether they used NRT; and (ii) whether they used NRT for TA. Demographic characteristics, daily cigarette consumption and whether a quit attempt had been made in the past 12 months were also assessed. FINDINGS Of the participants, 56% were attempting SR, 14% were using NRT for SR and 14% were using NRT for TA. Use of NRT for SR and TA were highly correlated. The nicotine patch was the most commonly used form of NRT. The use of NRT for SR, compared with unassisted SR, was more common among older smokers, while the use of NRT for TA was more common among women. Cigarette consumption was higher in those using NRT for SR than those attempting SR without NRT. The use of NRT for SR and TA was associated positively with past quit attempts. CONCLUSIONS Nicotine replacement therapy use for smoking reduction and temporary abstinence is common in England. The use of NRT for SR and TA does not appear to be associated with lower cigarette consumption relative to SR or TA without NRT, but is associated with a higher rate of past quit attempts.

Analysis and reporting of stepped wedge randomised controlled trials: synthesis and critical appraisal of published studies, 2010 to 2014

BackgroundStepped wedge cluster randomised trials introduce interventions to groups of clusters in a random order and have been used to evaluate interventions for health and wellbeing. Standardised guidance for reporting stepped wedge trials is currently absent, and a range of potential analytic approaches have been described.MethodsWe systematically identified and reviewed recently published (2010 to 2014) analyses of stepped wedge trials. We extracted data and described the range of reporting and analysis approaches taken across all studies. We critically appraised the strategy described by three trials chosen to reflect a range of design characteristics.ResultsTen reports of completed analyses were identified. Reporting varied: seven of the studies included a CONSORT diagram, and only five also included a diagram of the intervention rollout. Seven assessed the balance achieved by randomisation, and there was considerable heterogeneity among the approaches used. Only six reported the trend in the outcome over time. All used both ‘horizontal’ and ‘vertical’ information to estimate the intervention effect: eight adjusted for time with a fixed effect, one used time as a condition using a Cox proportional hazards model, and one did not account for time trends. The majority used simple random effects to account for clustering and repeat measures, assuming a common intervention effect across clusters. Outcome data from before and after the rollout period were often included in the primary analysis. Potential lags in the outcome response to the intervention were rarely investigated. We use three case studies to illustrate different approaches to analysis and reporting.ConclusionsThere is considerable heterogeneity in the reporting of stepped wedge cluster randomised trials. Correct specification of the time-trend underlies the validity of the analytical approaches. The possibility that intervention effects vary by cluster or over time should be considered. Further work should be done to standardise the reporting of the design, attrition, balance, and time-trends in stepped wedge trials.

Sample size calculation for a stepped wedge trial

BackgroundStepped wedge trials (SWTs) can be considered as a variant of a clustered randomised trial, although in many ways they embed additional complications from the point of view of statistical design and analysis. While the literature is rich for standard parallel or clustered randomised clinical trials (CRTs), it is much less so for SWTs. The specific features of SWTs need to be addressed properly in the sample size calculations to ensure valid estimates of the intervention effect.MethodsWe critically review the available literature on analytical methods to perform sample size and power calculations in a SWT. In particular, we highlight the specific assumptions underlying currently used methods and comment on their validity and potential for extensions. Finally, we propose the use of simulation-based methods to overcome some of the limitations of analytical formulae. We performed a simulation exercise in which we compared simulation-based sample size computations with analytical methods and assessed the impact of varying the basic parameters to the resulting sample size/power, in the case of continuous and binary outcomes and assuming both cross-sectional data and the closed cohort design.ResultsWe compared the sample size requirements for a SWT in comparison to CRTs based on comparable number of measurements in each cluster. In line with the existing literature, we found that when the level of correlation within the clusters is relatively high (for example, greater than 0.1), the SWT requires a smaller number of clusters. For low values of the intracluster correlation, the two designs produce more similar requirements in terms of total number of clusters. We validated our simulation-based approach and compared the results of sample size calculations to analytical methods; the simulation-based procedures perform well, producing results that are extremely similar to the analytical methods. We found that usually the SWT is relatively insensitive to variations in the intracluster correlation, and that failure to account for a potential time effect will artificially and grossly overestimate the power of a study.ConclusionsWe provide a framework for handling the sample size and power calculations of a SWT and suggest that simulation-based procedures may be more effective, especially in dealing with the specific features of the study at hand. In selected situations and depending on the level of intracluster correlation and the cluster size, SWTs may be more efficient than comparable CRTs. However, the decision about the design to be implemented will be based on a wide range of considerations, including the cost associated with the number of clusters, number of measurements and the trial duration.

Stepped wedge randomised controlled trials: systematic review of studies published between 2010 and 2014

BackgroundIn a stepped wedge, cluster randomised trial, clusters receive the intervention at different time points, and the order in which they received it is randomised. Previous systematic reviews of stepped wedge trials have documented a steady rise in their use between 1987 and 2010, which was attributed to the design’s perceived logistical and analytical advantages. However, the interventions included in these systematic reviews were often poorly reported and did not adequately describe the analysis and/or methodology used. Since 2010, a number of additional stepped wedge trials have been published. This article aims to update previous systematic reviews, and consider what interventions were tested and the rationale given for using a stepped wedge design.MethodsWe searched PubMed, PsychINFO, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Web of Science, the Cochrane Library and the Current Controlled Trials Register for articles published between January 2010 and May 2014. We considered stepped wedge randomised controlled trials in all fields of research. We independently extracted data from retrieved articles and reviewed them. Interventions were then coded using the functions specified by the Behaviour Change Wheel, and for behaviour change techniques using a validated taxonomy.ResultsOur review identified 37 stepped wedge trials, reported in 10 articles presenting trial results, one conference abstract, 21 protocol or study design articles and five trial registrations. These were mostly conducted in developed countries (n = 30), and within healthcare organisations (n = 28). A total of 33 of the interventions were educationally based, with the most commonly used behaviour change techniques being ‘instruction on how to perform a behaviour’ (n = 32) and ‘persuasive source’ (n = 25). Authors gave a wide range of reasons for the use of the stepped wedge trial design, including ethical considerations, logistical, financial and methodological. The adequacy of reporting varied across studies: many did not provide sufficient detail regarding the methodology or calculation of the required sample size.ConclusionsThe popularity of stepped wedge trials has increased since 2010, predominantly in high-income countries. However, there is a need for further guidance on their reporting and analysis.

Do people with diabetes understand their clinical marker of long-term glycemic control (HbA1c levels) and does this predict diabetes self-care behaviours and HbA1c?

OBJECTIVE Research demonstrates that patients have a poor understanding of glycosylated haemoglobin A1c (HbA1c) and that this impacts on effective diabetes self-management. This study attempted to replicate these findings in a UK outpatient sample of people with diabetes. METHOD 83 participants were recruited and asked to fill in a questionnaire assessing their understanding of HbA1c, diabetes self-care behaviours and diabetes-specific self-efficacy in relation to carrying out these self-care behaviours. RESULTS Only 26.5% of the participants were classified as having a good understanding of HbA1c. Correlational and univariate analyses indicated that this level of understanding was related to demographic variables, HbA1c levels and certain aspects of self-care and self-efficacy. A series of multiple regressions found that understanding was a significant predictor of HbA1c levels. CONCLUSION The majority of participants seemed to have a poor understanding of HbA1c and this was related to aspects of their diabetes management, self-efficacy and HbA1c levels. PRACTICAL IMPLICATIONS These findings provide support for the application of programmes and initiatives aimed at improving patients understanding of clinical disease markers.

Comparison of brief interventions in primary care on smoking and excessive alcohol consumption: a population survey in England

Background Brief interventions have a modest but meaningful effect on promoting smoking cessation and reducing excessive alcohol consumption. Guidelines recommend offering such advice opportunistically and regularly but incentives vary between the two behaviours. Aim To use representative data from the perspective of patients to compare the prevalence and characteristics of people who smoke or drink excessively and who receive a brief intervention. Design and setting Data was from a representative sample of 15 252 adults from household surveys in England. Method Recall of brief interventions on smoking and alcohol use, sociodemographic information, and smoking and alcohol consumption patterns were assessed among smokers and those who drink excessively (AUDIT score of ≥8), who visited their GP surgery in the previous year. Results Of 1775 smokers, 50.4% recalled receiving brief advice on smoking in the previous year. Smokers receiving advice compared with those who did not were more likely to be older (odds ratio [OR] 17-year increments 1.19, 95% confidence interval [CI] =1.06 to 1.34), female (OR 1.35, 95% CI =1.10 to 1.65), have a disability (OR 1.44, 95% CI = 1.11 to 1.88), have made more quit attempts in the previous year (compared with no attempts: one attempt, OR 1.65, 95% CI = 1.32 to 2.08; ≥2 attempts, OR 2.02, 95% CI =1.49 to 2.74), and have greater nicotine dependence (OR 1.17, 95% CI =1.05 to 1.31) but were less likely to have no post-16 qualifications (OR 0.81, 95% CI = 0.66 to 1.00). Of 1110 people drinking excessively, 6.5% recalled receiving advice in their GP surgery on their alcohol consumption in the previous year. Those receiving advice compared with those who did not had higher AUDIT scores (OR 1.17, 95% CI =1.12 to 1.23) and were less likely to be female (OR 0.44, 95% CI = 0.23 to 0.87). Conclusion Whereas approximately half of smokers in England visiting their GP in the past year report having received advice on cessation, <10% of those who drink excessively report having received advice on their alcohol consumption.

Five questions to consider before conducting a stepped wedge trial

Researchers should consider five questions before starting a stepped wedge trial.Why are you planning one? Researchers sometimes think that stepped wedge trials are useful when there is little doubt about the benefit of the intervention being tested. However, if the primary reason for an intervention is to measure its effect, without equipoise there is no ethical justification for delaying implementation in some clusters. By contrast, if you are undertaking pragmatic research, where the primary reason for rolling out the intervention is for it to exert its benefits, and if phased implementation is inevitable, a stepped wedge trial is a valid option and provides better evidence than most non-randomized evaluations.What design will you use? Two common stepped wedge designs are based on the recruitment of a closed or open cohort. In both, individuals may experience both control and intervention conditions and you should be concerned about carry-over effects. In a third, continuous-recruitment, short-exposure design, individuals are recruited as they become eligible and experience either control or intervention condition, but not both.How will you conduct the primary analysis? In stepped wedge trials, control of confounding factors through secular variation is essential. ‘Vertical’ approaches preserve randomization and compare outcomes between randomized groups within periods. ‘Horizontal’ approaches compare outcomes before and after crossover to the intervention condition. Most analysis models used in practice combine both types of comparison. The appropriate analytic strategy should be considered on a case-by-case basis.How large will your trial be? Standard sample size calculations for cluster randomized trials do not accommodate the specific features of stepped wedge trials. Methods exist for many stepped wedge designs, but simulation-based calculations provide the greatest flexibility. In some scenarios, such as when the intracluster correlation coefficient is moderate or high, or the cluster size is large, a stepped wedge trial may require fewer clusters than a parallel cluster trial.How will you report your trial? Stepped wedge trials are currently challenging to report using CONSORT principles. Researchers should consider how to demonstrate balance achieved by randomization and how to describe trends for outcomes in both intervention and control clusters.