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Digital interventions for alcohol can help achieve reductions in hazardous and harmful alcohol consumption. The Drink Less app was developed using evidence and theory, and a factorial randomized controlled trial (RCT) suggested that 4 of its intervention modules may assist with drinking reduction. However, low engagement is an important barrier to effectiveness, and low response to follow up is a challenge for intervention evaluation. Research is needed to understand what factors influence users’ level of engagement, response to follow up, and extent of alcohol reduction.
This study aimed to investigate associations between user characteristics, engagement, response to follow up, and extent of alcohol reduction in an app-based intervention, Drink Less.
This study involved a secondary data analysis of a factorial RCT of the Drink Less app. Participants (N=672) were aged 18 years or older, lived in the United Kingdom, and had an Alcohol Use Disorders Identification Test score >7 (indicative of excessive drinking). Sociodemographic and drinking characteristics were assessed at baseline. Engagement was assessed in the first month of use (number of sessions, time on app, number of days used, and percentage of available screens viewed). Response to follow up and extent of alcohol reduction (change in past week consumption) were measured after 1 month. Associations were assessed using unadjusted and adjusted linear or logistic regression models.
Age (all unstandardized regression coefficients [B] >.02, all
Users of the alcohol reduction app, Drink Less, who were older and had post-16 educational qualifications engaged more and were more likely to respond at 1-month follow up. Higher baseline alcohol consumption predicted a greater extent of alcohol reduction among those followed up but did not predict engagement or response to follow up. Engagement was associated with response to follow up but was not associated with the extent of alcohol reduction, which suggests that the Drink Less app does not have a dose-response effect.
International Standard Randomised Controlled Trial Number ISRCTN40104069; http://www.isrctn.com/ISRCTN40104069 (Archived by WebCite at http://www.webcitation.org/746HqygIV)
Excessive alcohol consumption is a priority for public health and has a large economic impact on society because of lost productivity, crime, and health care costs [
Eysenbach’s law of attrition distinguishes between 2 types of attrition: nonusage and dropout [
The law of attrition also proposes that engagement and response to follow up are positively associated: if users stop engaging with an intervention, then they are unlikely to respond to follow up [
To improve the likelihood of behavior change and the validity of DBCI trial’s results, research is needed to understand whether certain users are less likely to engage with the intervention, respond to follow up, or change behavior. The identification of predictors of engagement, response to follow up, and behavior change could inform the development of tailored strategies for specific user groups in DBCI trials. The relationship between engagement and response to follow up has not yet been evaluated in an app-based alcohol intervention.
Existing literature indicates that being female, older, and better educated predicts higher engagement and greater response to follow up in Web-based alcohol interventions [
Drinkers in England who report an attempt to reduce their drinking are more likely to be older, female, of higher socioeconomic status, have high levels of alcohol consumption, and less likely to be white [
This study investigated the associations between user characteristics, engagement, response to follow up, and extent of alcohol reduction in an app-based intervention, Drink Less, and addressed the following research questions:
What associations, if any, exist between user characteristics (sociodemographic and drinking) measured when drinkers register with the app and (a) engagement, (b) likelihood of response to follow up, and (c) extent of alcohol reduction at follow up among those followed up?
What associations, if any, exist between engagement and (a) likelihood of response to follow up and (b) extent of alcohol reduction at follow up among those followed up?
The study design is secondary data analysis from a factorial RCT of the Drink Less app between May and August 2016 (reported in full elsewhere [
Participants were eligible if they were: aged 18 years or above; lived in the United Kingdom; had an AUDIT score of 8 or above (indicative of excessive alcohol consumption warranting intervention [
Drink Less was designed as a stand-alone intervention available to anyone seeking digital support for reducing excessive alcohol consumption. It is centered on a goal-setting module with 5 intervention modules: (1) normative feedback, (2) cognitive bias retraining, (3) self-monitoring and feedback, (4) action planning, and (5) identity change. The app also contains standard features such as the AUDIT questionnaire and feedback on users’ results, the UK drinking guidelines, and links for additional support. Usability testing was conducted during the original app development to understand the user experience and refine the app [
Data collection began on May 18, 2016, and ended on August 28, 2016. On first opening the app, each user was provided with a participant information sheet and asked to provide consent to participate in the trial. Users who consented were asked to complete the AUDIT and a sociodemographic questionnaire, indicate whether they were interested in drinking less alcohol, and provide their email address for follow up. Users were then given their AUDIT score and informed of their
Engagement was measured as a continuous variable through automatic recording of the extent of DBCI use in terms of amount, depth, frequency, and duration in the 28-day period following registration [
Response to follow up was a binary (yes or no) measure of completion of the 1-month follow-up questionnaire. The extent of alcohol reduction was measured as the change in past week alcohol consumption (−90 to +90 units) derived from the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C; a brief screening test for alcohol consumption) between time of registration and 1-month follow up.
User characteristic variables were measured at baseline, on first opening the app, and assessed: age (continuous); gender (male or female); employment status (dichotomized into employed vs not employed); ethnicity (dichotomized into white vs not white); education (dichotomized into pre-16 and post-16 educational qualifications); whether they were a current smoker (yes or no); past week alcohol consumption derived from the AUDIT-C (ranging from 0 to 90 units), and full AUDIT score (ranging from 0 to 40).
All analyses were conducted using R version 3.4.0, and the analysis plan was preregistered on the Open Science Framework [
Generalized linear modeling (linear or logistic, as appropriate) was used to examine the associations between user characteristics (predictor variable) and engagement, response to follow up, or extent of alcohol reduction (outcome variables). Both unadjusted (univariate) and fully adjusted (multivariable) regression models were reported. Treatment group was included in all adjusted analyses as it is a factor relating to the DBCI that may predict engagement [
Generalized linear modeling (linear or logistic, as appropriate) was used to examine the associations between engagement (predictor variable) and response to follow up or extent of alcohol reduction (outcome variables). Both unadjusted and fully adjusted (for treatment group and any predictors of the outcome variables) regression models are reported.
Sensitivity analyses were conducted in which the engagement measures were dichotomized into high or low groups and entered in a logistic regression model to see whether the pattern of results differs.
A total of 672 participants were included. The mean age was 39.2 years; over half were females (377/672, 56.1%); and the majority were employed (581/672, 86.5%), white (640/672, 95.2%), and had post-16 years’ educational qualifications (484/672, 72.0%). About a quarter of participants (165/672, 24.6%) were current smokers and participants consumed a mean of 39.9 units of alcohol in the past week and had a mean AUDIT score of 19.1, indicating excessive alcohol consumption.
Age was significantly positively associated with all 4 measures of engagement. Education level was significantly positively associated with all 4 measures of engagement: number of sessions, time spent on the app (only when adjusted for other user characteristics and treatment group), the number of days on which the app was used, and the percentage of available screens viewed. Older users and those with post-16 educational qualifications were more likely to have a greater number of sessions, spend more time on the app, use the app on a greater number of days, and view a larger percentage of available screens.
Gender was significantly associated with the percentage of available screens viewed by the user in both unadjusted and adjusted models; users who were female viewed a greater percentage of screens available to them. No other user characteristics were associated with engagement with the Drink Less app.
User characteristics, engagement, response to follow up, and extent of alcohol reduction (N=672).
User characteristics | Statistics | |
Age (years), mean (SD) | 39.2 (10.9) | |
Female, n (%) | 377 (56.1) | |
Employed, n (%) | 581 (86.5) | |
White, n (%) | 640 (95.2) | |
Post-16 years, n (%) | 484 (72.0) | |
Yes, n (%) | 165 (24.6) | |
Past week alcohol consumption in units, mean (SD) | 39.9 (27.3) | |
AUDITb score, mean (SD) | 19.1 (6.6) | |
Number of sessions, median (IQRa) | 5 (2-17) | |
Time on app in min:s, median (IQR) | 17:14 (8:53-37:19) | |
Number of days used, median (IQR) | 4 (2-13) | |
Percentage of available screens viewed, mean (SD) | 39.0 (13.3) | |
Completion of 1-month follow up, n (%) | 179 (26.6) | |
Reduction in past week alcohol consumption in units, mean (SD) | 14.3 (24.1) |
aIQR: interquartile range.
bAUDIT: Alcohol Use Disorder Identification Test.
The effect of user characteristics on measures of engagement (number of sessions and time on app).
User characteristics | Sessions, median (IQRa) | Unadjusted simple linear regression | Adjustedb multiple regression | Time on app (min), median (IQR) | Unadjusted simple linear regression | Adjustedb multiple regression | ||||||
Bc (95% CI) | B (95% CI) | B (95% CI) | B (95% CI) | |||||||||
Age (years) | —d | .02 (0.02 to 0.03) | <.001 | .03 (0.02 to 0.03) | <.001 | — | .03 (0.02 to 0.03) | <.001 | .03 (0.02 to 0.03) | <.001 | ||
Male (reference; n=295) | 7 (2 to 18) | — | — | — | — | 16 (8 to 34) | — | — | — | — | ||
Female (n=377) | 5 (2 to 15) | .14 (−0.05 to 0.33) | .14 | .12 (−0.07 to 0.31) | .21 | 18 (9 to 41) | .13 (−0.02 to 0.29) | .09 | .11 (−0.03 to 0.26) | .13 | ||
Unemployed (reference; n=91) | 5 (1 to 15) | — | — | — | — | 15 (9 to 37) | — | — | — | — | ||
Employed (n=581) | 6 (2 to 17) | .23 (−0.04 to 0.51) | .10 | .27 (−0.01 to 0.54) | .06 | 18 (9 to 37) | .06 (−0.16 to 0.28) | .61 | .12 (−0.10 to 0.34) | .27 | ||
White (reference; n=640) | 5 (2 to 17) | — | — | — | — | 17 (9 to 38) | — | — | — | — | ||
Not white (n=32) | 6 (2 to 16) | −.09 (−0.54 to 0.35) | .68 | .01 (−0.43 to 0.45) | .96 | 17 (10 to 27) | −.06 (−0.42 to 0.29) | .74 | .04 (−0.30 to 0.39) | .81 | ||
Pre-16 (reference; n=188) | 4 (2 to 13) | — | — | — | — | 15 (8 to 34) | — | — | — | — | ||
Post-16 (n=484) | 6 (2 to 18) | .31 (0.10 to 0.52) | .004 | .36 (0.15 to 0.57) | <.001 | 18 (9 to 41) | .12 (−0.05 to 0.28) | .18 | .18 (0.02 to 0.16) | .03 | ||
Yes (reference; n=165) | 4 (2 to 16) | — | — | — | — | 15 (8 to 29) | — | — | — | — | ||
No (n=507) | 6 (2 to 18) | .20 (−0.02 to 0.42) | .08 | .01 (−0.22 to 0.23) | .96 | 18 (9 to 39) | .16 (−0.01 to 0.34) | .07 | −.02 (−0.19 to 0.16) | .84 | ||
Past week alcohol consumption (units)e | — | 0 (0) | .570 | 0 (0) | .75 | — | 0 (0) | .47 | 0 (0) | .37 | ||
Alcohol use (AUDITf score) | — | −.01 (−0.02 to 0) | .19 | −.01 (−0.02 to 0.01) | .42 | — | 0 (–0.01 to 0.01) | .64 | 0 (–0.01 to 0.01) | .94 |
aIQR: interquartile range.
bAdjusted for all sociodemographic variables, AUDIT score, and treatment group (unless otherwise specified).
cUnstandardized regression coefficient.
dNot applicable.
eAdjusted for all sociodemographic variables and treatment group (not AUDIT score).
fAUDIT: Alcohol Use Disorder Identification Test.
The effect of user characteristics on measures of engagement (number of days used and % of screens viewed).
User characteristics | Days used, median (IQRa) | Unadjusted simple linear regression | Adjustedb multiple regression | Screens viewed, % mean (SD) | Unadjusted simple linear regression | Adjustedb multiple regression | ||||||
Bc (95% CI) | B (95% CI) | B (95% CI) | B (95% CI) | |||||||||
Age (years) | —d | .02 (0.01 to 0.03) | <.001 | .02 (0.02 to 0.03) | <.001 | — | .25 (0.16 to 0.34) | <.001 | .28 (0.19 to 0.38) | <.001 | ||
Male (reference; n=295) | 4 (2 to 11) | — | — | — | — | 37.7 (13.54) | — | — | — | — | ||
Female (n=377) | 5 (2 to 13) | .12 (−0.05 to 0.30) | .16 | .10 (−0.07 to 0.27) | .25 | 40.0 (13.54) | 2.29 (0.27 to 4.32) | .03 | 1.99 (0.01 to 3.97) | .049 | ||
Unemployed (reference; n=91) | 3 (1 to 11) | — | — | — | — | 38.6 (13.97) | — | — | — | — | ||
Employed (n=581) | 4 (2 to 13) | .16 (−0.09 to 0.41) | .22 | .18 (−0.08 to 0.43) | .17 | 39.0 (13.22) | .37 (−2.58 to 3.32) | .80 | .07 (−2.88 to 3.02) | .96 | ||
White (reference; n=640) | 4 (2 to 13) | — | — | — | — | 39.1 (13.29) | — | — | — | — | ||
Not white (n=32) | 4 (1 to 13) | −.13 (−0.54 to 0.27) | .52 | −.04 (−0.44 to 0.36) | .83 | 36.9 (13.84) | −2.12 (−6.86 to 2.61) | .38 | −1.48 (−6.16 to 3.19) | .53 | ||
Pre-16 years (reference; n=188) | 3 (1 to 9) | — | — | — | — | 36.7 (13.27) | — | — | — | — | ||
Post-16 years (n=484) | 5 (2 to 14) | .23 (0.04 to 0.42) | .02 | .28 (0.10 to 0.47) | .003 | 39.8 (13.24) | 3.15 (0.91 to 5.38) | .006 | 4.04 (1.85 to 6.24) | <.001 | ||
Yes (reference; n=165) | 3 (1 to 12) | — | — | — | — | 37.8 (12.72) | — | — | — | — | ||
No (n=507) | 5 (2 to 13) | .20 (0 to 0.39) | .05 | .02 (−0.02 to 0.01) | .85 | 39.4 (13.49) | 1.57 (−0.77 to 3.91) | .19 | −.05 (−2.40 to 2.30) | .97 | ||
Past week alcohol consumption (units)e | — | 0 (–0.01 to 0) | .33 | 0 (0) | .42 | — | 0 (−0.04 to 0.04) | .98 | 0 (−0.03 to 0.04) | .87 | ||
Alcohol use (AUDITf score) | — | −.01 (−0.02 to 0) | .13 | −.01 (−0.02 to 0.01) | .28 | — | 0 (−0.15 to 0.16) | .99 | .02 (−0.13 to 0.17) | .82 | ||
aIQR: interquartile range.
bAdjusted for all sociodemographic variables, AUDIT score, and treatment group (unless otherwise specified).
cUnstandardized regression coefficient.
dNot applicable.
eAdjusted for all sociodemographic variables and treatment group (not AUDIT score).
fAUDIT: Alcohol Use Disorders Identification Test.
The effect of user characteristics on response to follow up.
User characteristics | Completed follow up, n (%) | Unadjusted simple logistic regression | Adjusteda multiple logistic regression | |||
ORb (95% CI) | OR (95% CI) | |||||
Age (years) | —c | 1.04 (1.02-1.05) | <.001 | 1.04 (1.02-1.06) | <.001 | |
Male (reference; n=295) | 63 (21.4) | — | — | — | — | |
Female (n=377) | 116 (30.8) | 1.64 (1.15-2.34) | .006 | 1.58 (1.09-2.29) | .02 | |
Unemployed (reference; n=91) | 31 (34.1) | — | — | — | — | |
Employed (n=581) | 148 (25.5) | 0.66 (0.42-1.07) | .09 | 0.66 (0.40-1.12) | .12 | |
White (reference; n=640) | 172 (26.9) | — | — | — | — | |
Not white (n=32) | 7 (21.9) | 0.76 (0.30-1.70) | .53 | 0.74 (0.28-1.73) | .51 | |
Pre-16 (reference; n=188) | 36 (19.1) | — | — | — | — | |
Post-16 (n=484) | 143 (29.5) | 1.77 (1.18-2.70) | .007 | 2.11 (1.38-3.29) | <.001 | |
Yes (reference; n=165) | 34 (20.6) | — | — | — | — | |
No (n=507) | 145 (28.6) | 1.54 (1.02-2.38) | .045 | 1.23 (0.79-1.95) | .37 | |
Past week alcohol consumption (units)d | — | 1.00 (0.99-1.01) | .92 | 1.00 (1.00-1.01) | .56 | |
Alcohol use (AUDITe score) | — | 1.00 (0.97-1.02) | .72 | 1.00 (0.97-1.03) | .95 |
aAdjusted for all sociodemographic variables, AUDIT score, and treatment group (unless otherwise specified).
bOR: odds ratio.
cNot applicable.
dAdjusted for all sociodemographic variables and treatment group (not AUDIT score).
eAUDIT: Alcohol Use Disorder Identification Test.
Sensitivity analyses were conducted in which the engagement measures were dichotomized into high or low groups based on their median score (except for percentage of available screens viewed, which was dichotomized based on the mean score). The pattern of results remained the same.
The effect of user characteristics on extent of alcohol reduction.
User characteristics | Mean (SD) | Unadjusted linear regression | Adjusteda multiple regression | ||||
Bb (95% CI) | B (95% CI) | ||||||
Age (years) | —c | −.13 (−0.44 to 0.18) | .42 | −.11 (−19.71 to 27.33) | .53 | ||
Male (reference; n=63) | 14.4 (26.25) | — | — | — | — | ||
Female (n=116) | 14.2 (22.97) | −.20 (−7.67 to 7.26) | .96 | .51 (−6.95 to 7.96) | .89 | ||
Unemployed (reference; n=31) | 10.9 (21.4) | — | — | — | — | ||
Employed (n=148) | 15.0 (24.63) | 4.05 (−5.35 to 13.45) | .40 | 5.07 (−4.73 to 14.89) | .31 | ||
White (reference; n=172) | 14.5 (24.28) | — | — | — | — | ||
Not white (n=7) | 8.6 (19.91) | −5.92 (−24.29 to 12.45) | .53 | −7.17 (−25.84 to 11.49) | .45 | ||
Pre-16 (reference; n=36) | 17.2 (26.49) | — | — | — | — | ||
Post-16 (n=143) | 13.6 (23.51) | −3.58 (−12.46 to 5.30) | .43 | −2.96 (−11.93 to 6.01) | .52 | ||
Yes (reference; n=34) | 15.9 (25.42) | — | — | — | — | ||
No (n=145) | 13.9 (23.86) | −1.97 (−11.05 to 7.11) | .67 | .45 (−8.91 to 9.81) | .93 | ||
Past week alcohol consumption (units)d | — | .49 (0.37 to 0.61) | <.001 | .49 (0.37 to 0.62) | <.001 | ||
Alcohol use (AUDITe score) | — | 1.01 (0.46 to 1.55) | <.001 | .98 (0.40 to 1.55) | <.001 |
aAdjusted for all sociodemographic variables, AUDIT score, and treatment group (unless otherwise specified).
bUnstandardized regression coefficient.
cNot applicable.
dAdjusted for all sociodemographic variables and treatment group (not AUDIT score).
eAUDIT: Alcohol Use Disorder Identification Test.
The association between engagement and response to follow up.
Engagement measures | Statistics | Unadjusted simple logistic regression | Adjusteda multiple logistic regression | |||
ORb (95% CI) | OR (95% CI) | |||||
Did not respond (reference) | 4 (2-11) | —d | — | — | — | |
Responded | 19 (8-32) | 1.08 (1.06-1.10) | <.001 | 1.08 (1.06-1.09) | <.001 | |
Did not respond (reference) | 13.2 (7.4-26.0) | — | — | — | — | |
Responded | 35.1 (18.9-70.9) | 1.02 (1.02-1.03) | <.001 | 1.02 (1.02-1.03) | <.001 | |
Did not respond (reference) | 3 (1-8) | — | — | — | — | |
Responded | 14 (6-24) | 1.14 (1.11-1.17) | <.001 | 1.13 (1.11- 1.16) | <.001 | |
Did not respond (reference) | 35.6 (12.14) | — | — | — | — | |
Responded | 48.3 (11.92) | 1.09 (1.07-1.11) | <.001 | 1.09 (1.07-1.11) | <.001 |
aAdjusted for treatment group, age, gender, and education group (as significant predictors of response to follow up).
bOR: odds ratio.
cIQR: interquartile range.
dNot applicable.
The association between engagement and extent of alcohol reduction.
Engagement measures | Unadjusted linear regression | Adjusteda multiple linear regression | ||
Bb (95% CI) | B (95% CI) | |||
Sessions | −.16 (−0.37 to 0.05) | .13 | −.14 (−0.34 to 0.07) | .19 |
Time on app | −.06 (−0.13 to 0.01) | .08 | −.06 (−0.13 to 0.01) | .07 |
Days used | −.18 (−0.57 to 0.21) | .37 | −.10 (−0.48 to 0.28) | .61 |
Available screens viewed | −.07 (−0.37 to 0.23) | .66 | −.09 (−0.39 to 0.22) | .58 |
aAdjusted for treatment group and baseline AUDIT score (as a significant predictor of extent of alcohol reduction).
bUnstandardized regression coefficient.
Users who were older and had post-16 educational qualifications engaged with the Drink Less app to a greater extent, which was indicated by number of sessions, time on app, number of days used, and percentage of available screens viewed. Female users viewed a significantly greater percentage of available screens compared with male users. Users who were older, female, and had post-16 educational qualifications were also significantly more likely to respond to follow up. In line with previous literature from Web-based alcohol interventions [
All 4 measures of engagement were positively associated with the likelihood of responding to follow up, and this association remained when adjusting for the user characteristics that were significant predictors of response to follow up, which replicated previous findings [
Past week alcohol consumption and AUDIT score were both positively associated with the extent of alcohol reduction among those followed up. None of the sociodemographic characteristics were associated with the extent of alcohol reduction. There has been a lack of research on the predictors of alcohol reduction in the general population, particularly in app-based interventions. Our study found that drinking characteristics were positively associated with the extent of alcohol reduction. This is likely explained by regression to the mean, as measurements that differ substantially from the true mean tend to be followed by measurements closer to the true mean [
No associations were detected between the engagement measures and the extent of alcohol reduction among those followed up, and these results were robust to a sensitivity analysis with the engagement measures as dichotomous variables. This means that we were not able to determine whether there is a threshold level of engagement with the app that would achieve users’ intended reduction in alcohol consumption. These findings conflict previous findings of a positive association between engagement measures and successful behavior change [
Tailored strategies for younger male users with lower educational qualifications, who tend to have lower levels of engagement and response to follow up, could be codeveloped with these users to improve engagement and response to follow up. Users who were older and had post-16 educational qualifications engaged with the app to a greater extent in terms of number of sessions, time spent on the app, the number of days it was used for, and the depth of their use. The app was not designed for a specific age group (other than the adult population) and involved user testing with participants from disadvantaged groups who typically have poorer Web-based literacy to ensure it was usable and acceptable to these groups [
The finding that engagement measures were not associated with the extent of alcohol reduction suggests that engagement measures should not be used as a proxy for behavior change and that greater levels of engagement are not necessarily required to achieve a desired change in behavior. Therefore, tailored strategies for improving engagement and response to follow up will not necessarily result in the desired behavior change.
To our knowledge, this is the first study to investigate the predictors of engagement, response to follow up, and extent of alcohol reduction in an app-based intervention. Drink Less is freely available in the UK iTunes App Store and users were not directly recruited for a trial; instead, they downloaded the app and were then recruited to the trial. Therefore, this sample has high ecological validity and represents the real-world situation for most users of behavior change apps. This study had a modest sample size and could be repeated with a larger sample to assess whether the findings are replicable. A limitation of this study is that the measures of engagement used were summative and, therefore, could not be used to assess more specific patterns of engagement (eg, the order in which users engaged with the app’s different components), which future research should look to investigate.
Users of an alcohol reduction app who were older and had post-16 educational qualifications engaged to a greater extent. These characteristics and being female predicted users being more likely to respond to a follow-up questionnaire 1 month later. Higher baseline levels of alcohol consumption were predictive of a greater extent of alcohol reduction, but were not predictive of engagement or response to follow up. Engagement measures were significantly associated with response to follow up, in line with the law of attrition. Engagement measures were not associated with the extent of alcohol reduction, which suggests that there is no dose-response effect of the Drink Less app.
Alcohol Use Disorders Identification Test
Alcohol Use Disorders Identification Test-Consumption
Cancer Research UK
digital behavior change intervention
interquartile range
National Institute for Health Research
odds ratio
School for Public Health Research
randomized controlled trial
UK Centre for Tobacco and Alcohol Studies
CG, IT, JB, and RW are funded by Cancer Research UK (CRUK: C1417/A22962). OP is funded by a grant from Bupa under its partnership with University College London. SM’s contribution is funded by Cancer Research UK (CRUK) and the National Institute for Health Research (NIHR) School for Public Health Research (SPHR). Drink Less was funded by the NIHR SPHR, the UK Centre for Tobacco and Alcohol Studies (UKCTAS), the Society for the Study of Addiction, and CRUK. The views expressed are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, or the Department of Health. The research team is part of the UKCTAS, a UK Clinical Research Collaboration Public Health Research Centre of Excellence. Funding from the Medical Research Council, British Heart Foundation, Cancer Research UK, Economic and Social Research Council, and the National Institute for Health Research under the auspices of the UK Clinical Research Collaboration is gratefully acknowledged. The funders played no role in the design, conduct, or analysis of the study, or in the interpretation or reporting of study findings.
JB has received unrestricted research grants from Pfizer related to smoking cessation. RW has received research funding and has undertaken consultancy for companies that manufacture smoking cessation medications. CG, OP, IT, and SM have no competing interests.