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A wide range of effective smoking cessation interventions have been developed to help smokers to quit. Smoking rates remain high, especially among people with a lower level of education. Multiple tailoring adapted to the individual’s readiness to quit and the use of visual messaging may increase smoking cessation.
The results of video and text computer tailoring were compared with the results of a control condition. Main effects and differential effects for subgroups with different educational levels and different levels of readiness to quit were assessed.
During a blind randomized controlled trial, smokers willing to quit within 6 months were assigned to a video computer tailoring group with video messages (n=670), a text computer tailoring group with text messages (n=708), or to a control condition with short generic text advice (n=721). After 6 months, effects on 7-day point prevalence abstinence and prolonged abstinence were assessed using logistic regression analyses. Analyses were conducted in 2 samples: (1) respondents (as randomly assigned) who filled in the baseline questionnaire and completed the first session of the program, and (2) a subsample of sample 1, excluding respondents who did not adhere to at least one further intervention session. In primary analyses, we used a negative scenario in which respondents lost to follow-up were classified as smokers. Complete case analysis and multiple imputation analyses were considered as secondary analyses.
In sample 1, the negative scenario analyses revealed that video computer tailoring was more effective in increasing 7-day point prevalence abstinence than the control condition (OR 1.45, 95% CI 1.09-1.94,
In all analyses, video computer tailoring was effective in realizing smoking cessation. Furthermore, video computer tailoring was especially successful for smokers with a low readiness to quit smoking. Text computer tailoring was only effective for sample 2. Results suggest that video-based messages with personalized feedback adapted to the smoker’s motivation to quit might be effective in increasing abstinence rates for smokers with diverse educational levels.
Netherlands Trial Register: NTR3102; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=3102 (Archived by WebCite at http://www.webcitation.org/6NS8xhzUV).
A wide range of different smoking cessation interventions have been developed and implemented. In spite of this, smoking rates remain high, especially among people with lower levels of education [
The Internet has become a promising method of delivering smoking cessation interventions and has increased opportunities to reach large numbers of people [
To date, Web-based computer-tailored smoking cessation interventions delivered via the Internet often consist of simple text-based messages. However, this might be not attractive enough for Internet users, especially less-educated groups [
Another strategy to improve the success of computer-tailored smoking cessation interventions is by focusing on the smoker’s motivation to quit smoking. Until now, most computer-tailored smoking cessation interventions have been developed for smokers with high motivation to quit [
The study described in this paper was designed to investigate the effectiveness of 2 computer-tailored smoking cessation interventions after 6 months: (1) a text-based multiple computer-tailored intervention where smokers received tailored text-based messages during several feedback moments, and (2) a video-based multiple computer-tailored intervention where smokers received tailored video-based messages during several feedback moments. In both interventions, smokers with high or low readiness to quit were able to choose different routings and received tailored feedback adapted to their readiness to quit. The effectiveness of the 2 interventions was compared to a control condition (respondents received a generic short text advice).
We hypothesized video-based computer tailoring to be more effective for smokers with a lower level of education, whereas text-based computer tailoring was expected to be more effective in smokers with a higher level of education. Because the interventions included different routings tailored according to the smokers’ readiness to quit, we expected less-motivated smokers to be equally successful in their quit attempts as more motivated smokers. Therefore, we explored whether the effects of the 2 interventions were different for individuals with a high or low readiness to quit. Moreover, we conducted our analyses in 2 different samples: (1) respondents who filled in baseline questionnaire and completed the first session of the program, and (2) a subsample of sample 1, excluding respondents who did not adhere to at least one further intervention session [
The current study was submitted for approval to the Medical Research Ethics Committee (MREC) of Atrium Medical Centre Heerlen. The MREC decided that no MREC approval was necessary because respondents were not required to undertake any particular action. The study was registered at the Dutch Trial Register (NTR3102). The study was in-line with the ethical codes of conduct of the American Psychological Association (APA) [
Respondents were recruited from December 2010 to June 2012 to participate in the Web-based multiple computer-tailored smoking cessation intervention. Respondents were eligible for participation if they were motivated to quit smoking within the next 6 months, were 18 years or older, and had access to the Internet.
Respondents were recruited by several channels. First, a random sample of approximately 150 general practitioners (GPs) was asked to refer smoking patients to the intervention website. The GP practices were provided with recruitment materials (flyers, business cards, etc) for this purpose. Second, respondents were also recruited to participate through advertising campaigns in local newspapers, newspaper websites, and Dutch health fund websites. Lastly, we used several national and international online social networking websites, such as Hyves and Facebook, to invite smokers to participate in our smoking cessation study. All advertisements provided a link to the intervention website that enabled people to find out more information about the intervention and participation.
The current study was a randomized controlled trial with 2 experimental conditions (text-based computer tailoring vs video-based computer tailoring) and a control condition in which respondents received only a single generic short text advice. Interested respondents could sign up via the intervention website [
After giving online informed consent, respondents were asked to fill out the baseline questionnaire. Respondents in the text-based and video-based condition received tailored feedback over 3 months (see Intervention and
The 2 Web-based multiple computer-tailored smoking cessation interventions (text-based vs video-based computer tailoring) varied only in their mode of delivery (see
Intervention design of a video- and text-based computer-tailored intervention for smoking cessation following 2 routings.
Respondents who had set a goal to quit within 1 month were directed to routing 1.The goal of routing 1 was to help smokers translate their intention to quit into action by providing tailored feedback to increase self-efficacy and effective action planning. In the first session, after receiving feedback on their smoking behavior, attitude, social influences, and self-efficacy with respect to quitting, respondents were asked to choose a quit date (between 8 days and 1 month from the first session). At the end of this first session, respondents were informed that they would be invited to the next session 1 week before their quit date, to receive help with quitting. During the second session (1 week before their quit attempt), respondents received feedback on the extent to which they had already made concrete plans for their quit attempt because past research has revealed that preparing for quitting increases the likelihood of quitting [
Respondents who were not ready to quit within 1 month were directed to routing 2. The goal of routing 2 was to increase motivation by increasing perception of the pros of quitting and knowledge of how to obtain support for quitting. In session 1, directly after completion of the baseline assessment, smokers were encouraged to use the following month to reflect on their smoking behavior and motivation to quit. In session 2, 1 month after baseline, respondents were invited by email for the next session. Respondents received tailored feedback on their smoking behavior, their attitude (pros and cons of smoking and quitting), and their perceived social support. Next, they were invited to indicate their readiness to quit smoking. Respondents who indicated an intention to quit within 1 month were directed to routing 1 and were asked to set a quit date. Respondents who were not ready to quit received an invitation to take part in the next session (session 3); this session used a similar strategy that was used in session 2. Respondents ready to quit were directed to routing 1 and were asked to set a quit date. Respondents who indicated at the end of session 3 that they were not prepared to quit received a kind message indicating that the intervention program would respect the fact that they were not ready to quit smoking and that they would receive no further invitations.
The content of the feedback messages was exactly the same in both the text- and video-based conditions. In the text-based condition, respondents received multiple sessions of text-based computer-tailored advice without any graphics or animations. In the video-based condition, the same tailored advice was presented by adults in a video message. Five different adult presenters (2 males, 3 females) were selected out of a screening test of 20 persons who delivered the tailored advice in a TV news program format. We used a mix of adults during the different sessions who presented the different pieces of tailored advice.
The following demographic variables were assessed: age, gender (0=male; 1=female), educational level (1=low corresponding to primary, basic vocational, lower general school, or no education; 2=intermediate corresponding to higher general secondary education, preparatory academic education, or medium vocational school; 3=high corresponding to higher vocational school or university level), and nationality (0=other nationality; 1=Dutch nationality).
Addiction level was measured by 6 items using the Fagerström Test for Nicotine Dependence (FTND), asking respondents how many cigarettes they smoked per day, at which time points, and whether they had difficulties not smoking in smoke-free places (0=not addicted; 10=highly addicted) [
Readiness to quit smoking was assessed with a single item asking respondents whether and when they intended to quit smoking, resulting in 3 categories (1=yes, within 4 to 6 months; 2=yes, within 1 to 3 months; 3=yes, within the following month) [
Smoking habit was assessed using an abbreviated version of Verplanken and Orbell’s Self-Reported Habit Index of 6 items (eg, smoking is something which I do automatically) with which respondents could agree or disagree, resulting in a 5-point scale (1=I totally disagree; 5=I totally agree). A mean scale score was included in the analyses (Cronbach alpha=.78) [
Depressive symptoms were measured with the abbreviated 10-item Center for Epidemiologic Studies Depression scale (CES-D) that asked respondents whether they felt depressed during the past week, for example, resulting in a 4-point scale (1=rarely or none of the time; 4=most or all of the time) [
Occurrence of smoking-related diseases was measured by 4 questions on a dichotomous scale, such as “Do you suffer from chronic obstructive pulmonary disease (COPD), cancer, diabetes, or cardiovascular disease?” (0=no; 1=yes).
Attitude was measured by 3 items assessing the pros and cons of quitting (quitting smoking would be reasonable, bad, or enjoyable), resulting in a 5-point scale (1=I totally disagree; 5=I totally agree). A mean scale score was included in the analyses (Cronbach alpha=.52). A higher score represents a positive attitude toward quitting.
Social influence was measured by 2 scales: a social modeling and a social support scale. Social modeling was assessed by 2 items that measured whether other people in their environment smoked, such as partners (1=no, 2=yes, 9=not applicable), and in their social environment, such as family or friends (1=none, 2=a minority, 3=half, 4=a majority, 5=all, 9=not applicable). A total of 552 respondents for the partner question and 80 respondents for the social environment question filled in “not applicable” when they were asked whether their partner or their social environment smoked. Social support was measured with 2 items that asked whether smokers received social support (partners and social environment, respectively) in favor of quitting on a 4-point scale (1=no, 2=yes, a bit, 3=yes, moderate, 4=yes, a lot, 9=not applicable). A total of 787 respondents for the partner question and 229 for the social environment question filled in “not applicable” when they were asked whether they received support from their partner or their social environment. Not applicable was recoded into the lowest value (1=no support) for the social influence measure. The items were summed and formed an index that was included in the analyses.
Preparatory plans were assessed by 3 items that measured whether participants planned to execute different preparatory plans for their quit attempt (removing ashtrays, telling their environment to quit smoking, quitting without decreasing smoking first) on a 5-point scale (1=surely not; 5=surely yes). The items were summed and formed an index that was included in the analyses.
Coping plans were assessed by 4 items that measured whether participants had made specific plans to prevent relapse in difficult situations, such as plans how to cope with negative mood, plans how to cope when being at a party or drinking a cup of coffee, or being offered a cigarette (0=no; 1=yes). Difficult situations were selected and predefined based on previous studies [
Self-efficacy was measured by 3 items asking respondents whether they would be able to refrain from smoking in these difficult situations (Do you think you will manage not to smoke when you drink a cup of coffee, when you are in a negative mood, or when you visit a party?), resulting in a 5-point scale (1=definitely not; 5=yes, definitely). A mean scale score was included in the analyses (Cronbach alpha=.62).
The variables attitude, self-efficacy, preparatory plans, and coping plans were also used to determine the tailored advice during the first session of the intervention.
At the 6-month follow-up measurement, 7-day point prevalence abstinence was self-assessed by 1 item asking respondents whether they had refrained from smoking during the past 7 days (0=no; 1=yes) [
In addition, prolonged abstinence was self-assessed by 1 item asking respondents whether they had refrained from smoking since their last quit attempt allowing for a 2-week grace period during which the respondent could smoke 1 to 5 cigarettes (0=no;1=yes) [
Process evaluation was conducted by measuring 5 concepts, each measured on a 5-point scale (1=totally disagree to 5=totally agree):
Attention to the tailored advice (eg, the advice was interesting) was measured by 3 items (Cronbach alpha=.94).
Comprehension of the advice (eg, the advice was clear to me) was measured by 3 items (Cronbach alpha=.78).
Adaptation toward the advice (eg, the advice was personally relevant for me) was measured by 3 items (Cronbach alpha=.79).
Appreciation of the advice (eg, I appreciated the advice) was measured by 3 items (Cronbach alpha=.93).
Processing of the advice (eg, the advice encouraged me to think more about smoking cessation) was measured by 8 items. For all process evaluation scales a mean scale score was included in the analyses (Cronbach alpha=.91).
The inclusion of all randomly assigned respondents is a common approach to analyze the effects of an intervention [
As a preliminary, descriptive analyses were conducted to check for baseline differences between the 3 conditions. Chi-square tests were used for categorical variables whereas analyses of variance (ANOVAs) were used for continuous variables. If the chi-square test showed a
Third, logistic regression analyses were conducted to investigate the effectiveness of the intervention on the outcome measures assessed at the 6-month follow-up measurements. The analyses were performed adjusting for potential confounders, including demographic variables (eg, age, educational level, gender, and ethnicity) and possible moderators of the intervention effect (eg, addiction level, recruitment strategy, readiness to quit smoking, depression, smoking-related illnesses, self-efficacy, preparatory planning, and coping planning), baseline differences, dropout predictors and 2 interaction terms (readiness to quit smoking by condition and educational level by condition). Where significant interaction terms were found, stratified analyses were performed separately for each group.
In the effect analyses, a negative scenario was used in which every respondent missing at follow-up was regarded as a smoker. In addition, we also used multiple imputation [
Lastly, we also conducted complete case analyses, in which we only took respondents into account who filled out the 6-month follow-up measurements (these results are presented in
Baseline sample characteristics for the video-based computer tailoring (video), text-based computer tailoring (text), and control conditions (recruited between December 2010 and June 2012).
Variables | Overall sample |
Video |
Text |
Control |
|
Tukey HSD/ |
|
Gender (female), n (%) | 1278 (60.9) | 417 (62.2) | 431 (60.9) | 430 (59.6) | .61 |
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Age (years), mean (SD) | 45.7 (12.8) | 45.5 (13.0) | 45.4 (12.8) | 46.2 (12.5) | .46 |
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.41 |
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Low | 705 (33.6) | 225 (33.6) | 231 (32.6) | 249 (34.5) |
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Medium | 782 (37.3) | 247 (36.9) | 255 (36.0) | 280 (38.8) |
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High | 612 (29.2) | 198 (29.5) | 222 (31.4) | 192 (26.6) |
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Dutch nationality, n (%) | 1995 (95.2) | 639 (95.5) | 674 (95.2) | 682 (94.9) | .85 |
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FTNDc score (1-10), mean (SD) | 4.9 (2.4) | 5.0 (2.3) | 4.9 (2.4) | 4.9 (2.5) | .46 |
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Number of cigarettes smoked per day, mean (SD) | 18.8 (8.6) | 19.0 (8.1) | 18.7 (8.4) | 19.0 (9.2) | .75 |
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.005 | Video/text<control | |
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Within 1 month | 1093 (52.1) | 368 (54.9) | 384 (54.2) | 341 (47.3) |
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Within 1-3 months | 636 (30.3) | 205 (30.6) | 203 (28.7) | 228 (31.6) |
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Within 4-6 months | 370 (17.6) | 97 (14.5) | 121 (17.1) | 152 (21.1) |
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With COPD diseases | 290 (13.8) | 97 (14.5) | 99 (14.0) | 94 (13.0) | .73 |
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With cancer | 34 (1.6) | 10 (1.5) | 9 (1.3) | 15 (2.1) | .46 |
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With diabetes | 99 (4.7) | 27 (4.0) | 33 (4.7) | 39 (5.4) | .48 |
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With cardiovascular diseases | 210 (10.0) | 63 (9.3) | 60 (8.5) | 87 (12.1) | .06 |
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With asthmatic diseases | 171 (8.1) | 63 (9.4) | 57 (8.1) | 51 (7.1) | .28 |
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.76 |
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General practitioner | 166 (7.9) | 56 (8.4) | 57 (8.1) | 53 (7.4) |
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Newspaper/Internet | 1631 (77.7) | 511 (76.3) | 551 (77.8) | 569 (78.9) |
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Family/friends | 203 (9.7) | 69 (10.3) | 72 (10.2) | 62 (8.6) |
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Other strategies | 99 (4.7) | 34 (5.1) | 28 (4.0) | 37 (5.1) |
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Depressive feelings | 5.8 (2.4) | 5.9 (2.5) | 5.8 (2.4) | 5.8 (2.4) | .42 |
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Habit | 4.0 (0.6) | 4.0 (0.6) | 4.0 (0.6) | 4.0 (0.7) | .50 |
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Social support | 5.1 (1.8) | 5.1 (1.7) | 5.2 (1.8) | 5.1 (1.9) | .41 |
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Social modeling | 3.9 (1.2) | 4.0 (1.2) | 4.0 (1.3) | 3.9 (1.2) | .15 |
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Attitude | 4.2 (0.7) | 4.2 (0.7) | 4.1 (0.7) | 4.2 (0.7) | .53 |
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Self-efficacy | 3.2 (0.9) | 3.2 (0.9) | 3.1 (0.9) | 3.1 (0.9) | .22 |
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Preparatory planning | 11.0 (2.6) | 11.2 (2.6) | 11.0 (2.6) | 10.8 (2.6) | .008 | Video>control | |
Coping planning | 1.2 (1.5) | 1.5 (1.6) | 1.3 (1.5) | 0.96 (1.5) | <.001 | Video/text>control |
aAnalyses of variance (ANOVAs,
bTukey honestly significant difference (HSD), alpha=.05; Bonferroni-corrected alpha=.05/3=.017.
cFTND: Fagerström Test for Nicotine Dependence.
Flowchart of participant enrollment and inclusion. Sample 1: all randomly assigned respondents; sample 2: only respondents in the experimental conditions who adhered to at least one session.
Six-month abstinence rates, including 7-day point prevalence abstinence (PPA) and prolonged abstinence (PA), for the video-based computer tailoring (video), text-based computer tailoring (text), and control conditions for sample 1 (negative scenario).
Negative scenario | Total, N | Condition, n (%) |
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Video | Text | Control |
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2099 | 140 (20.9) | 127 (17.9) | 105 (14.6) | .008 | |
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PA | 2099 | 98 (14.6) | 99 (14.0) | 87 (12.1) | .34 |
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PA | 2099 |
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Within 1 month |
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62 (16.8) | 71 (18.5) | 52 (15.2) | .51 |
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Within 1-3 months |
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22 (10.7) | 18 (8.9) | 30 (13.2) | .36 |
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Within 4-6 months |
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14 (14.4) | 10 (8.3) | 5 (3.3) | .006 |
When respondents lost to follow-up were regarded as smokers in the analyses, no significant interaction was found between the type of condition and educational level (χ2
4=6.3,
Factors associated with 7-day point prevalence abstinence in sample 1 (negative scenario) in the present study.
Negative scenario variablesa | Sample 1 (N=2099) | ||
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OR | 95% CI |
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Video vs control | 1.45 | 1.09-1.94 | .01 |
Text vs control | 1.22 | 0.92-1.63 | .17 |
Gender (male) | 0.90 | 0.70-1.14 | .38 |
Age | 1.01 | 1.00-1.02 | .07 |
Dutch nationality | 1.23 | 0.71-2.15 | .46 |
Middle education levelb | 1.08 | 0.81-1.45 | .58 |
High education levelb | 1.17 | 0.87-1.59 | .30 |
Readiness to quit within 1 monthc | 1.71 | 1.16-2.50 | .006 |
Readiness to quit within 1-3 monthsc | 1.41 | 1.17-2.10 | .09 |
FTND score | 0.96 | 0.91-1.00 | .07 |
CES-D score | 0.94 | 0.89-.99 | .03 |
With COPDd | 1.03 | 0.71-1.50 | .86 |
With cancerd | 1.00 | 0.40-2.50 | .99 |
With diabetesd | 1.22 | 0.69-2.20 | .51 |
With cardiovascular diseasesd | 1.18 | 0.78-1.78 | .43 |
With asthmad | 0.89 | 0.58-1.39 | .61 |
Recruitment strategy, newspaper/Internete | 0.67 | 0.45-0.99 | .04 |
Preparatory planning | 1.07 | 1.02-1.12 | .009 |
Coping planning | 1.01 | 0.94-1.10 | .72 |
Self-efficacy | 1.15 | 1.01-1.33 | .04 |
aInteraction terms are not included in the final model because they were not significant and ORs are adjusted for variables significant at baseline and dropout.
bLow education is the reference category.
cWillingness to quit within 4-6 months is the reference category.
dNot suffering from the disease is the reference category.
eGeneral practitioner (GP) is the reference category.
In the negative scenario, no significant interaction was found between condition and educational level on prolonged abstinence (χ2
4=3.1,
Factors associated to prolonged abstinence in sample 1 (negative scenario) in the present study.
Negative scenario | Sample 1 (N=2099) | ||||
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ORa | 95% CI |
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Video vs control | 5.13 | 1.76-14.92 | .003 | |
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Text vs control | 2.79 | 0.92-8.46 | .07 | |
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Gender (male) | 0.72 | 0.54-0.95 | .02 | |
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Age | 1.01 | 1.00-1.02 | .05 | |
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Dutch nationality | 1.31 | 0.69-2.44 | .40 | |
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Middle education levelb | 1.16 | 0.84-1.61 | .35 | |
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High education levelb | 1.01 | 0.72-1.43 | .94 | |
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Readiness to quit within 1 monthc | 4.18 | 1.61-10.85 | .003 | |
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Readiness to quit within 1-3 monthsc | 4.11 | 1.55-10.95 | .005 | |
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FTND score | 0.95 | 0.89-1.00 | .04 | |
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CES-D score | 0.90 | 0.84-.96 | .002 | |
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With COPDd | 1.23 | 0.80-1.89 | .34 | |
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With cancerd | 0.66 | 0.26-1.66 | .37 | |
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With diabetesd | 1.04 | 0.55-1.96 | .90 | |
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With cardiovascular diseasesd | 1.24 | 0.78-1.89 | .37 | |
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With asthma | 1.12 | 0.67-1.90 | .66 | |
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Recruitment strategy, newspaper/Internete | 0.62 | 0.41-0.95 | .03 | |
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Preparatory planning | 1.08 | 1.02-1.14 | .007 | |
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Coping planning | 1.07 | 0.98-1.17 | .14 | |
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Self-efficacy | 1.18 | 1.01-1.38 | .04 | |
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High readiness to quit × video | 0.21 | 0.07-0.66 | .007 | |
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High readiness to quit × text | 0.45 | 0.12-1.45 | .18 | |
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Middle readiness to quit × video | 0.15 | 0.04-0.51 | .002 | |
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Middle readiness to quit × text | 0.23 | 0.06-0.81 | .02 | |
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Video vs text | 0.86 | 0.59-1.27 | .46 |
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Video vs control | 1.07 | 0.71-1.62 | .74 |
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Text vs control | 1.24 | 0.83-1.86 | .29 |
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Video vs text | 1.23 | 0.63-2.40 | .54 |
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Video vs control | 0.77 | 0.43-1.41 | .40 |
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Text vs control | 0.63 | 0.34-1.18 | .15 |
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Video vs text | 1.84 | 0.77-4.40 | .17 |
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Video vs control | 5.13 | 1.76-14.92 | .003 |
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Text vs control | 2.80 | 0.92-8.46 | .07 |
aORs are adjusted for variables significant at baseline and dropout.
bLow education is the reference category.
cWillingness to quit within 4-6 months is the reference category.
dNot suffering from the disease is the reference category.
eGeneral practitioner (GP) is the reference category.
Abstinence rates per educational level for the video-based and text-based computer tailoring interventions, stratified by adherence.
Condition | Abstinent |
χ2 2 |
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0.0 | .99 | ||
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Low educational level | 47 (20.9) |
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Middle educational level | 51 (20.6) |
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High educational level | 42 (21.2) |
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8.0 | .02 | ||
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Low educational level | 33 (14.3) |
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Middle educational level | 41 (16.1) |
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High educational level | 53 (23.9) |
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10.4 | .001 | |
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Adherence=0 (n=128) | 17 (13.3) |
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Adherence>1 (n=97) | 30 (30.9) |
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16.5 | <.001 | |
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Adherence=0 (n=135) | 15 (11.1) |
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Adherence>1 (n=112) | 36 (32.1) |
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29.9 | .04 | |
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Adherence=0 (n=113) | 18 (15.9) |
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Adherence>1 (n=85) | 24 (28.2) |
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3.5 | .06 | |
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Adherence=0 (n=218) | 8 (8.9) |
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Adherence>1 (n=238) | 25 (17.7) |
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10.1 | .001 | |
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Adherence=0 (n=242) | 8 (7.5) |
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Adherence>1 (n=260) | 33 (22.3) |
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3.4 | .07 | |
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Adherence=0 (n=177) | 10 (15.6) |
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Adherence>1 (n=243) | 43 (27.2) |
|
|
Means and standard deviations (SD) for evaluation of different aspects of the video-based and text-based computer tailoring intervention programs at 6-month follow-up.
Evaluation items | Overall sample |
Video |
Text |
|
Feedback was attractive (attendance), mean (SD) | 3.40 (1.04) | 3.49 (1.07) | 3.37 (1.03) | .18 |
Feedback was understandable (comprehensibility), mean (SD) | 3.63 (0.70) | 3.69 (0.70) | 3.58 (0.70) | .15 |
Feedback fit to own situation (adaptation), mean (SD) | 3.31 (0.74) | 3.35 (0.78) | 3.28 (0.71) | .40 |
Feedback was useful (appreciation), mean (SD) | 3.54 (0.96) | 3.64 (1.02) | 3.45 (0.90) | .07 |
Feedback helped to make quit attempt (processing), mean (SD) | 3.27 (0.86) | 3.37 (0.90) | 3.20 (0.82) | .06 |
Overall grade of feedback (from 1-10), mean (SD) | 6.45 (1.62) | 6.60 (1.67) | 6.34 (1.57) | .15 |
The aim of this study was to evaluate the effects and appreciation of 2 multiple computer-tailored smoking cessation interventions (video- vs text-based messages) delivered via the Internet, regarding 6-month smoking abstinence among different educational groups. To our knowledge, this study is one of the first studies to test the effects of mode of delivery in the context of smoking cessation. Low levels of adherence may lead to an underestimation of the effects; therefore, the effectiveness of the 2 computer-tailored interventions was assessed by analyzing 2 samples. The first sample included all randomly assigned respondents who filled in the baseline questionnaire and followed the first session of the intervention whereas the second sample was a subsample of sample 1 including only respondents (in the experimental conditions) who adhered at least to one further session of the intervention.
Our study revealed several important findings. In contrast to our expectations, the results of all analyses revealed no significant differences in quit rates between smokers with low and high educational levels in the 2 experimental conditions (video- vs text-based messages). However, in both samples, the video-based computer-tailored smoking cessation intervention was effective in increasing 7-day point prevalence abstinence. The text-based computer-tailored smoking cessation intervention, however, was only significantly effective in increasing 7-day point prevalence abstinence in people who adhered to at least one further session (after baseline and session 1). The video-based condition was also more effective compared to the text-based condition regarding 7-day point prevalence abstinence in sample 2.
Moreover, with regard to prolonged abstinence our study revealed a differential effect of the intervention between people with a low or high readiness to quit, consistent with our second expectation. In sample 1, the video-based computer-tailored intervention appeared to be especially successful in increasing prolonged abstinence rates among smokers with a lower readiness to quit (within 4-6 months), whereas in sample 2, the video-based computer-tailored intervention was also effective among smokers willing to quit within 1 month. The multiple imputation and the complete case analyses yielded comparable results, a finding that may be attributed to the fact that rates of missing data were not extremely high at 6-month follow-up (on average 30%).
Consistent with previous findings [
Our study revealed another interesting effect of the video-tailored intervention for people with a lower readiness to quit smoking. With different routings available in our smoking cessation intervention, we expected that both interventions would be effective for people with a lower motivation to quit at baseline. Partially consistent with our second hypothesis, the results revealed that only the video-tailored intervention appeared to be successful in smokers with a lower readiness to quit. The availability of different intervention routings provided these less-motivated smokers the possibility to reflect on their smoking behavior and their potential quit attempt; these less-motivated smokers may have benefited from this option in the video-based condition.
Consistent with our expectation, our study showed that abstinence rates were higher overall when respondents adhered to at least one further intervention element. In sample 2, adherence can be regarded as a determinant of the efficacy of the program. These findings are in-line with different previous research, which also found that the efficacy of a program increased when people adhered to the intervention [
To our knowledge, this is the first study to assess the effectiveness of a Web-based tailored video and text intervention aiming to promote smoking cessation in groups with varying levels of education and varying levels of readiness to quit. A strength of the study is that 2 different sensitivity analyses were performed to test the robustness of our results. However, our study is also subject to several limitations. First, a misreport may have occurred when respondents were asked to indicate their smoking status at the 6-month follow-up measurement. For financial reasons, we were not able to biochemically validate respondents’ self-assessed smoking status. Although future Web-based intervention studies might be recommended to verify smoking status through the use of biochemical cotinine test as part of a more detailed follow-up assessment, it is also argued that this might be irrelevant (eg, if anonymity has been guaranteed) [
Despite these limitations, the present study provides evidence that video-based messages are successful in stimulating quitting behavior. As past research has already indicated that Internet users prefer to receive content in the form of video-based messages [
The current study provides important new evidence for the effectiveness of a video-based computer-tailored smoking cessation intervention. The results suggest that a video-based computer-tailored intervention with personalized feedback adapted to the smokers’ motivation to quit might be effective in increasing abstinence rates for smokers with different educational levels. The results support the feasibility of using video messaging to affect smoking behavior. We measured smoking abstinence after 6 months; more research is needed to examine whether these results persist over longer follow-up periods.
Results of regression analysis on sample 2.
Results of complete case regression analysis.
CONSORT-EHEALTH checklist V1.6.2 [
Center for Epidemiologic Studies Depression scale
chronic obstructive pulmonary disease
Fagerström Test for Nicotine Dependence
general practitioner
honestly significant difference
Medical Research Ethics Committee
The authors would like to thank all respondents for taking part in the study. This work was supported by ZonMw, the Netherlands Organisation for Health Research and Development (grant number: 20011007).
Hein de Vries is scientific director of Vision2-Health, a company that licenses evidence-based computer-tailored health communication tools.