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Distributing a multiple computer-tailored smoking cessation intervention through the Internet has several advantages for both provider and receiver. Most important, a large audience of smokers can be reached while a highly individualized and personal form of feedback can be maintained. However, such a smoking cessation program has yet to be developed and implemented in the Netherlands.
To investigate the effects of a Web-based multiple computer-tailored smoking cessation program on smoking cessation outcomes in a sample of Dutch adult smokers.
Smokers were recruited from December 2009 to June 2010 by advertising our study in the mass media and on the Internet. Those interested and motivated to quit smoking within 6 months (N = 1123) were randomly assigned to either the experimental (n = 552) or control group (n = 571). Respondents in the experimental group received the fully automated Web-based smoking cessation program, while respondents in the control group received no intervention. After 6 weeks and after 6 months, we assessed the effect of the intervention on self-reported 24-hour point prevalence abstinence, 7-day point prevalence abstinence, and prolonged abstinence using logistic regression analyses.
Of the 1123 respondents, 449 (40.0%) completed the 6-week follow-up questionnaire and 291 (25.9%) completed the 6-month follow-up questionnaire. We used a negative scenario to replace missing values. That is, we considered respondents lost to follow-up to still be smoking. The computer-tailored program appeared to have significantly increased 24-hour point prevalence abstinence (odds ratio [OR] 1.85, 95% confidence interval [CI] 1.30–2.65), 7-day point prevalence abstinence (OR 2.17, 95% CI 1.44–3.27), and prolonged abstinence (OR 1.99, 95% CI 1.28–3.09) rates reported after 6 weeks. After 6 months, however, no intervention effects could be identified. Results from complete-case analyses were similar.
The results presented suggest that the Web-based computer-tailored smoking cessation program had a significant effect on abstinence reported after a 6-week period. At the 6-month follow-up, however, no intervention effects could be identified. This might be explained by the replacement of missing values on the primary outcome measures due to attrition using a negative scenario. While results were similar when using a less conservative scenario (ie, complete-case analyses), the results should still be interpreted with caution. Further research should aim at identifying strategies that will prevent high attrition in the first place and, subsequently, to identify the best strategies for dealing with missing data when studies have high attrition rates.
Dutch Trial Register NTR1351; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=1351 (Archived by WebCite at http://www.webcitation.org/67egSTWrz)
Worldwide, the smoking of tobacco is the most preventable cause of illness and premature death [
The Internet has been discovered to be a popular gateway for delivering health behavior change interventions in general [
Although a key element of computer tailoring is that the intervention materials are adapted to specific respondent characteristics, some smokers might benefit more than others from particular smoking cessation interventions. For example, the level of nicotine dependence has previously been suggested to moderate the effectiveness of smoking cessation interventions [
As Web-based multiple tailored smoking cessation feedback has not yet been offered to the Dutch general public outside scientific studies, our research group developed a Web-based multiple computer-tailored smoking cessation program and offered Dutch adult smokers the opportunity to participate in this program. The present study investigated the effectiveness of this program on smoking cessation outcomes reported after 6 weeks and 6 months. To imitate a natural situation in which smokers who do not participate in a smoking cessation program do not receive the intervention, the control group did not receive any of the intervention’s components. Nevertheless, both the intervention and control group were free to use other smoking cessation aids during the study period. In addition, we investigated whether the effect of the intervention was different for specific subgroups of smokers and whether we could detect a dose–response relationship between the number of feedback messages received and abstinence at the last follow-up.
The Web-based multiple computer-tailored smoking cessation program was based on a previously developed effective single computer-tailored intervention [
All respondents in the experimental condition received at least one tailored feedback letter (ie, at baseline). At the 6-week follow-up, respondents could have received at most two tailored feedback letters (ie, at baseline and 2 days after their set quit date), and at the 6-month follow-up, they could have received a maximum of three tailored feedback letters (ie, at baseline, 2 days after their set quit date, and at the 6-week follow-up).
Screenshot of items regarding the pros of smoking cessation.
Screenshot of personal advice regarding the pros of smoking cessation.
This study was approved by the Medical Ethics Committee of Maastricht University and the University Hospital Maastricht (MEC 08-3-037; NL22692.068.08), and is registered with the Dutch Trial Register (NTR1351). A full description of the study protocol is provided elsewhere [
We recruited adult smokers from December 2009 to June 2010 by advertising our study in the mass media and on the Internet. We sent several press releases to regional newspapers in the Netherlands. Most of these newspapers subsequently mentioned our study on their website, included an item about the project in the print version of their newspaper, or mentioned our study on their local radio station or television channel, or both. We also used a Dutch online social network website (Hyves) and several online smoking cessation forums to disseminate our recruitment text. In addition, we advertised our study in a free national newspaper obtainable at all Dutch train stations and several other public places throughout the Netherlands.
After 12 months, we expected a 10% point prevalence abstinence rate in the control condition. Based on results from previous projects, we expected the multiple tailoring program to lead to a 20% point prevalence abstinence rate. To be able to detect this difference significantly (alpha = 5%, beta = 10%), according to a 2-tailed Fisher exact test, 281 respondents per arm were required at the end of the trial (562 respondents in total) [
Interested smokers could sign up for the study on the study website (http://www.persoonlijkstopadvies.nl) and were eligible to participate if they were 18 years of age or older, were motivated to quit smoking within 6 months, and had access to the Internet. On the study website, participants were informed that the study was financed by the Dutch Cancer Society and conducted by researchers from Maastricht University in cooperation with the Dutch Expert Center on Tobacco Control (STIVORO). Additionally, the website included information about the objectives of the study, the randomization procedure and the incentive provided when respondents completed all questionnaires (ie, a €10 voucher). Respondents could choose their own username and password and were informed that no one but the research team was able to retrieve these passwords. As respondents had to report their email address when signing up for the study, we could easily flag respondents with multiple identities and remove them from further analyses. After providing online informed consent, participants were randomly assigned to the intervention group or the control group by a computer software randomization device, allocating approximately 50% of all respondents to each group. Blinding of respondents was not possible, as they had to take notice of whether they were receiving tailored feedback.
All questionnaires used in the present study were previously used and tested among Dutch smoking adults and were self-administered online [
We measured six demographic variables: age, gender (1 = male, 2 = female), educational level (1 = low: primary school/basic vocational school, 2 = medium: secondary vocational school/high school degree, 3 = high: higher vocational school/college degree/university degree), nationality (1 = Dutch, 2 = non-Dutch), and the occurrence of cardiovascular and respiratory diseases (1 = no, 2 = yes).
Exclusion criteria were based on current smoking behavior and motivation to quit smoking: current smoking behavior was measured by 1 item asking whether the respondent had smoked during the past 7 days (1 = no, 2 = yes). Motivation to quit smoking was measured by an adapted version of the Stage of Change algorithm [
We measured overall tobacco consumption using five open-ended questions regarding the number of cigarettes, hand-rolled cigarettes, cigars, cigarillos, and pipes smoked per day. Subsequently, the answers on these five questions were converted into an overall score for tobacco consumption (expressed as number of cigarettes), whereby 1 hand-rolled cigarette or cigarillo equaled 1 cigarette and 1 cigar equaled 4 cigarettes [
We measured addiction level by the abbreviated Fagerström Test for Nicotine Dependence (0 = not addicted, 10 = highly addicted) [
We assessed the number of past quit attempts with 1 item, asking the respondents how often they had tried to quit smoking in the past.
At the 6-week and 6-month follow-ups, we assessed prolonged abstinence by 1 item asking whether the respondent had refrained from smoking since the previous measurement (1 = no, 2 = yes). At the 6-week follow-up, prolonged abstinence referred to abstinence since the questionnaire that respondents received 2 days after their set quit date (ie, at least 2 weeks of abstinence). At the 6-month follow-up, this measure referred to abstinence since the 6-week follow-up (ie, 4.5 months of abstinence). In addition, at both follow-ups we assessed 24-hour and 7-day point prevalence abstinence, each by 1 item asking whether the respondent had refrained from smoking during the past 24 hours or 7 days (1 = no, 2 = yes).
First, we conducted descriptive analyses to determine the sample’s characteristics. To check for differences between the intervention and control groups, we conducted 2-sided
Second, we conducted logistic regression analyses to determine whether the intervention had an effect on the outcome measures assessed after follow-up periods of 6 weeks and 6 months. A negative scenario was used to replace missing values. That is, respondents lost to follow-up were considered to still be smoking. To test the robustness of the results, these analyses were also conducted with complete cases only.
Third, to determine whether the effect of the intervention was different for specific subgroups of smokers, we investigated whether we could identify interaction effects between the study condition and baseline demographic or behavioral measures using logistic regression analyses.
Data were analyzed using SPSS 17.0 (IBM Corporation, Somers, NY, USA). The significance level used was
Respondents included in the analyses had a mean age of 49.5 years; 535 (47.6%) were male; and 513 (45.7%) had a medium level of education. Respondents in the experimental group significantly differed from those in the control condition in their level of education (χ2
2 = 6.11,
Baseline sample characteristics of Dutch smoking adults (N = 1123) recruited from December 2009 to June 2010.
Characteristic | Overall sample |
Experimental group |
Control group |
|
Age (years), mean (SD) | 49.5 (32.5) | 48.4 (12.2) | 48.8 (12.3) | |
Male, % (n) | 47.6% (535) | 45.8% (253) | 49.4% (282) | |
|
||||
High | 21.2% (238) | 19.6% (108) | 22.8% (130) | |
Medium | 45.7% (513) | 43.8% (242) | 47.5% (271) | |
Low | 33.1% (372) | 36.6% (202) | 29.8% (170) | |
Dutch, % (n) | 97.7% (1097) | 97.8% (540) | 97.5% (557) | |
With cardiovascular diseases, % (n) | 9.4% (106) | 11.1% (61) | 7.9% (45) | |
With respiratory diseases, % (n) | 14.3% (161) | 12.5% (69) | 16.1% (92) | |
Number of cigarettes smoked/day, mean (SD) | 20.6 (12.4) | 20.8 (13.7) | 20.4 (11.0) | |
FTNDa score (range 1–10), mean (SD) | 5.1 (2.5) | 5.0 (2.5) | 5.2 (2.4) | |
Number of previous quit attempts, mean (SD) | 5.4 (17.5) | 5.1 (10.1) | 5.7 (22.4) |
a Fagerström Test for Nicotine Dependence.
As
Comparison between respondents followed up and respondents lost to follow-up after 6 weeks and 6 months.
Characteristic | 6-week follow-up | 6-month follow-up | |||
Followed up |
Lost to follow-up |
Followed up |
Lost to follow-up |
||
Age (years), mean (SD) | 50.1 (12.2) | 49.4 (12.6) | 50.0 (12.2)* | 48.1 (12.3)* | |
Male, % (n) | 44.5% (200) | 49.7% (335) | 45.5% (133) | 48.4% (402) | |
In experimental condition, % (n) | 49.9% (224) | 48.7% (328) | 49.3% (144) | 49.1% (408) | |
|
|||||
High | 19% (85) | 22.7% (153) | 19% (54) | 22.1% (184) | |
Medium | 45.9% (206) | 45.5% (307) | 45.2% (132) | 45.8% (381) | |
Low | 35.2% (158) | 31.8% (214) | 36.3% (106) | 32.0% (266) | |
Dutch, % (n) | 98.2% (441) | 97.3% (656) | 97.3% (284) | 97.8% (813) | |
With cardiovascular diseases, % (n) | 12% (52) | 8% (54) | 11% (31) | 9% (75) | |
With respiratory diseases, % (n) | 15% (68) | 14% (93) | 17% (49) | 13.5% (112) | |
Number of cigarettes smoked/day, mean (SD) | 19.8 (12.1) | 17.8 (6.1) | 19.5 (11.4) | 21.0 (12.7) | |
FTNDa score (range 1–10), mean (SD) | 4.8 (2.3) | 4.6 (2.3) | 4.7 (2.3)* | 5.2 (2.5)* | |
Number of previous quit attempts, mean (SD) | 5.0 (10.6) | 5.5 (5.9) | 5.1 (10.0) | 5.6 (19.5) |
a Fagerström Test for Nicotine Dependence.
*
Flow of respondents from enrollment in the study to allocation to the experimental and control conditions, retention, and whether they were included in the analysis.
Of the 552 respondents in the intervention group, 91 (17%) reported that they had refrained from smoking during the past 24 hours, 74 (13%) reported that they had not smoked during the past 7 days, and 60 (11%) reported that they had not smoked since the previous measurement 2 days after their quit date. In the control group (n = 571) these numbers were 55 (10%), 38 (7%), and 33 (6%), respectively. The intervention had a significant effect on all outcome measures, even when controlling for the baseline difference between the intervention and control groups with regard to their level of education (
After 6 months, a total of 51 (9%) respondents in the intervention group reported having refrained from smoking during the past 24 hours, 45 (8%) reported not having smoked during the past 7 days, and 23 (4%) reported not having smoked since the previous measurement. In the control group these numbers were 36 (6%), 34 (6%), and 19 (3%), respectively.
We investigated interaction effects between condition and baseline demographic or behavioral measures, although none of these turned out to have a significant influence on any of the abstinence measures reported after 6 weeks or 6 months (data not reported).
Effects of the Web-based smoking cessation intervention on several behavioral outcomes at 6-week follow-up among Dutch adult smokers (N = 1123) recruited from December 2009 to June 2010.
Model | 24-hour ppaa | 7-day ppa | Prolonged abstinence | |||||||
ORb | 95% CIc |
|
OR | 95% CI |
|
OR | 95% CI |
|
||
Interventiond | 1.85 | 1.30–2.65 | .001* | 2.17 | 1.44–3.27 | <.001* | 1.99 | 1.28–3.09 | .002* | |
|
1.81 | 1.26–2.59 | .001* | 2.16 | 1.43–3.25 | <.001* | 1.96 | 1.26–3.05 | .003* | |
Medium educatione | 0.81 | 0.51–1.32 | .42 | 0.75 | 0.45–1.36 | .28 | 0.75 | 0.41–1.32 | .31 | |
High educatione | 1.29 | 0.81–2.08 | .29 | 0.97 | 0.58–1.64 | .91 | 1.08 | 0.61–1.90 | .80 |
a Point prevalence abstinence.
b Odds ratio.
c Confidence interval.
d Control group is the reference category.
e Low education is the reference category.
*
Effects of the Web-based smoking cessation intervention on several behavioral outcomes at 6-month follow-up among Dutch adult smokers (N = 1123) recruited from December 2009 to June 2010.
Model | 24-hour ppaa | 7-day ppa | Prolonged abstinence | |||||||
ORb | 95% CIc |
|
OR | 95% CI |
|
OR | 95% CI |
|
||
Interventiond | 1.51 | 0.97–2.35 | .07 | 1.40 | 0.88–2.22 | .16 | 1.26 | 0.68–2.34 | .46 | |
|
1.47 | 0.94–2.30 | .09 | 1.38 | 0.87–2.20 | .17 | 1.29 | 0.69–2.41 | .42 | |
Medium educatione | 0.88 | 0.48–1.62 | .69 | 0.86 | 0.47–1.58 | .62 | 0.59 | 0.28–1.24 | .16 | |
High educatione | 1.38 | 0.76–2.52 | .29 | 1.10 | 0.59–2.05 | .76 | 0.56 | 0.25–1.26 | .16 |
a Point prevalence abstinence.
b Odds ratio.
c Confidence interval.
d Control group is the reference category.
e Low education is the reference category.
In the present study we investigated the effects of a multiple computer-tailored smoking cessation program delivered through the Internet. The results presented suggest significant effects of the intervention on short-term abstinence: at the 6-week follow-up, respondents who received the intervention were more likely to report being abstinent for the past 24 hours, for the past 7 days, and since the previous measurement (ie, 2 days after their quit date) than those who did not receive the intervention. Despite incorporating goal and relapse prevention strategies (action and coping plans), however, we found no effect of the intervention on abstinence measures assessed after 6 months.
A potential explanation for not finding any suggestion of intervention effects on long-term abstinence might be that more than 70% of the values on the primary outcome measures had to be replaced, as our study had relatively high levels of attrition, as have many previously developed Web-based interventions [
Another possible explanation for the lack of intervention effects on long-term abstinence may be that Web-based smoking cessation programs are not sufficiently tailored and adapted to the long-term wishes of recent ex-smokers to prevent relapse to smoking. Respondents received feedback only at fixed points in time; it was not possible to obtain additional personal feedback or support at times when smokers might have needed it most. The integration of ecological momentary assessment, by collecting real-time data through, for example, palmtops, personal digital assistants, or electronic diaries, might be promising. Studies using palmtop computers showed that a decrease in self-efficacy, an increase in positive smoking outcome expectancies, and an increase in negative affect predicted the occurrence of a lapse to smoking on the next day [
We found no support for different intervention effects for specific subgroups of smokers. Based on the results, it could thus be argued that the intervention was equally effective for all smokers who participated in the program. However, respondents who dropped out of the study were relatively more addicted and relatively younger than those who remained in the study, which is in line with previous research [
Major strengths of the present study were the large sample of smokers who initiated participation in the smoking cessation program and the relatively long follow-up period. However, as mentioned previously, the study had relatively high dropout rates. In the present study, we applied several strategies previously suggested to prevent attrition [
This Web-based computer-tailored smoking cessation program had a significant effect on abstinence measured after a 6-week follow-up period. However, this effect had entirely disappeared after 6 months. To prevent relapse, future studies should focus on the possibility of applying an ecological momentary assessment or combining the present Web-based intervention with the use of smoking cessation medication. Moreover, further research should aim at identifying strategies to prevent smokers from dropping out of Web-based smoking cessation interventions. As complete-case analyses and the replacement of missing values using a negative scenario both have their limitations, alternative strategies should be identified and tested.
Complete-case analyses.
CONSORT-EHEALTH Checklist (V1.6) [
confidence interval
odds ratio
This study was funded by the Dutch Cancer Society (UM 2007-3834).
Hein de Vries is scientific director of Vision2Health, a company that licenses evidence-based innovative computer-tailored health communication tools.