During the last decade, an increasing number of people used the Internet to obtain health-related information. In the field of health promotion, the Internet has become an important medium for the delivery of behavioral change interventions [1,2]. Health professionals have started to deliver several lifestyle behavior interventions through the web, including interventions aimed at smoking cessation [3], nutrition behavior [4], and physical activity [5,6].

A successful interactive strategy consists of computer-tailored interventions [7] through which individuals receive personalized information and feedback on health behavior and motives for this behavior. Tailored health messages are based on a person’s answers to a questionnaire on individual characteristics related to health behavior. Current research has shown the superiority of tailored materials over existing standard materials [8,9]. In the field of smoking cessation, recent studies have revealed computer-tailored smoking cessation interventions to be more effective than non-tailored interventions [9,10]. Yet, as with many eHealth interventions, smoking cessation interventions delivered via the Internet have high attrition rates, especially among people with lower education [11,12]. However, less educated people are often those who smoke more cigarettes [13] and show higher nicotine addiction rates, less quit-attempts, and more relapses compared to people with a higher level of education [14,15]. Past research in the Netherlands has shown that smoking prevalence was significantly higher among less educated people (29%) compared to people with a higher level of education (20%) [13]. Smokers of lower socioeconomic status (LSES) are therefore a highly relevant target group for using these computer-tailored programs. Hence, it is important to investigate how attrition rates among lower educated groups can be reduced and how computer-tailored interventions can be optimized to attract groups of different education levels.

A possible reason for the high attrition rates in computer-tailored interventions is that they rely heavily on text-based messages. Research suggests that video-based messages might be more effective in attracting attention and stimulating comprehension in people with a lower educational level [16,17]. Furthermore, video-based messages have been shown to require less mental effort and may help the person focus on the core elements of the message [18,19]. It is therefore conceivable that video-based messages may be better for reaching lower educated groups and realizing behavior change. Additionally, higher educated groups may benefit more from in-depth processing and accordingly may be stimulated more by text-based messages [20].

Recent research examining computer tailoring identified different underlying message processing-mechanisms that play an important role in enhancing health communication [21]. In tailored communication, five important message-processing mechanisms have been identified: attention, comprehension, processing, self-reference, and appreciation. Attention refers to the ability to focus on the receiving message. Due to the fact that paying less attention to a message lowers the overall effect, one purpose of tailored material is to increase the attention to the message. A recent study has shown stronger attention processes in people reading tailored material [22]. Comprehension refers to the ability to understand the content of the message. Past studies have shown that the better the message was matched to personal attributes and preferences, the more the message was understood and remembered [23,24]. A psychological theory that has been used to explain the effects of tailoring is the Elaboration Likelihood Model (ELM). This theory states there are two processing routes, the peripheral and the central. Related to this theory, personally relevant messages are processed by the “central route” and therefore take more effort to process [25]. The idea of tailoring is to increase the perceived relevance of the message in order to elicit a careful consideration of the message, which leads to a deeper impact of the received content. Indeed, a study about weight loss information indicated participants engaged in deeper processing of tailored information when compared to non-tailored information [26]. Besides effortful processing, a further aim of tailoring is to stimulate self-referential thinking. Self-referential thinking refers to the ability to refer the given information to one’s own situation. For example, tailored weight loss materials have been shown to encourage participants to link the information to their own situation [26]. Furthermore, it is also theorized that tailoring influences the appraisal of a message. Tailored materials may enhance the feeling of being well understood and would therefore lead to more appreciation compared to non-tailored information. To sum up, tailoring is used to increase the relevance of a health message by stimulating attention, comprehension, and the overall depth of message processing. The effects of tailoring on message-processing mechanisms might be further increased by the use of a suitable delivery strategy. However, as already indicated, these possible effects might be different for people with a lower or higher educational level.

To our knowledge, no previous studies have empirically examined the effects of delivery strategies (video vs text) and their impact on message-processing mechanisms among different educational groups. The first aim of this paper was to explore whether there exist differences in message-processing mechanisms according to delivery strategy (video or text based messages) and educational level. Past research has shown that information processing in lower educated groups was less profound and more influenced by visual than textual information [27]. Based on this, it was expected that tailoring would result in deeper information processing in lower educated smokers who received the video-based messages. In contrast, it was expected that the effects of tailoring would increase for higher educated smokers who received the text-based messages.

Additionally, eHealth research has acknowledged the importance of user experience of the intervention website. User experience refers to what a person thinks and feels during and after being exposed to a website [28]. Past research demonstrated the importance of user experiences (eg, trustworthiness, enjoyment) with regard to revisiting the website [29]. A positive user experience is related to an increased website use [28], resulting in future adherence [30]. Future adherence can comprise two components: (1) the intention to revisit an Internet-delivered intervention, and (2) recommending an Internet-delivered intervention to others [30]. Internet-delivered interventions and especially computer-tailored interventions often consist of several modules/feedback sessions. Since several sessions are often necessary to achieve behavior change in the long run [31,32], it is important to know whether people would like to revisit the intervention website. People with a high intention to revisit the website intervention might be likely to follow further important parts/sessions of the intervention and could therefore benefit more from the intervention than people leaving the program after their first visit. Next, it is important to know whether people would recommend the Internet-delivered intervention to others since previous studies demonstrated that the “word of mouth” strategy is effective in order to increase the use of eHealth interventions [33]. In order for web-based interventions to have an impact on public health, it is important that the intervention is also disseminated by the target population [34]. Now that computer-tailored interventions have become an increasingly popular strategy in the field of smoking cessation interventions, it is important to examine whether future adherence of computer-tailored intervention websites is influenced by delivery strategy (video vs text) and whether these effects vary among different educational groups.

The second aim of this study was to investigate whether there exist differences in future adherence according to delivery strategy (video or text based messages) and educational level. It was expected that future adherence would increase in lower educated smokers who received the video-based messages; whereas, we expected the same effects for higher educated smokers who received the text-based messages.

In summary, the current study was designed to assess whether a different delivery strategy (video vs text) interacted with educational level on message-processing mechanisms and future adherence of a Dutch computer-tailored smoking cessation program.

The first objective of the present study was to investigate whether differences exist in message-processing mechanisms according to delivery strategy and education level for a computer-tailored smoking cessation intervention. Results revealed that delivery strategy did not interact with education on message-processing mechanisms. This means that delivery strategy had no influence on the processing of the message by participants with different educational levels. Moreover, the processing of the tailored information did not differ between the two conditions. The idea that the processing of computer-tailored information would depend on the delivery strategy did not hold for this computer-tailored intervention. This conclusion is in line with results found in a recent study concerning physical activity, which demonstrated no differences between video- and text-generated computer-tailored messages [46]. One explanation for these findings may be the exposure time of the intervention. Participants may have needed to follow more than one session of the intervention in order to detect differences between the two delivery modes. Next, our findings revealed that lower educated smokers paid more attention to the tailored advice and seemed to process information more deeply. As such, lower educated smokers seemed to be more involved in the computer-tailored intervention than higher educated smokers. The findings imply that the current intervention session succeeded in approaching those smokers in the general population who might profit the most from these computer-tailored interventions. Our findings correspond with those from a recent study that found that lower educated participants were more likely to finish a module of a computer-tailored lifestyle intervention [47]. We did not find other differences between educational groups with respect to the other message-processing mechanisms: comprehension, self-reference, and appreciation, which is contrary to previous research that did find higher appreciation rates of computer-tailored advice among lower educated participants [48].

The second objective of this study was to examine whether differences exist in future adherence (intention to revisit, recommend the website to others) with regard to delivery strategy and education. The results revealed no interaction between delivery strategy and education on the intention to adhere; implying that a different delivery strategy did not influence future adherence of lower and higher educated smokers. Again, delivery strategy was found to have no particular effect on future adherence. However, the results demonstrated that lower educated smokers were more inclined to revisit the website compared to higher educated smokers. Although not significant, our findings revealed a slightly better appreciation of the website by lower educated participants, which may have contributed to the positive intention to revisit the website. As already mentioned, this is in line with findings of a previous study that lower educated participants were more inclined to initiate a lifestyle program [47]. Additionally, our lower educated participants were slightly less motivated (P<.11) to quit immediately, which also could explain a need for continued help to prepare them to quit in the future. Although lower educated smokers were more inclined to visit the website again, we could not find any educational differences with regard to recommending the website to others. As the results indicate that the “word of mouth”’ strategy might be not sufficient to recruit participants for Internet interventions, other recruitment strategies may also be needed such as recruitment through general practitioners [49]. A question raised by our results is why delivery strategy did not influence message-processing mechanisms and future adherence among different educational groups. One explanation could be that we recruited persons via an Internet research agency, which may have more innovative members that have already an open mind to Internet interventions. It might be possible that for those people, delivery mode did not differ that much. It is conceivable that delivery mode preference might have been different if we had recruited participants via other strategies, eg, newspaper advertisements. Furthermore, it might be possible that participants were not engaged by the design of the intervention components. The two conditions might have been presented in a more engaging way with further images, graphics, hyperlinks, and other animation effects. However, an important precondition for our experimental design was that the information of the video condition was contingent on the information provided by text condition to reveal the added effect of presenting information through a video format.

Our findings reveal that the tailored advice given was more positively evaluated by lower educated smokers. This outcome is in contrast with earlier studies indicating that web-based programs may result in a digital divide between lower and higher educated groups. Perhaps high Internet use among the Dutch (over 90%) may explain these effects [37]. Moreover, since our less educated smokers often show higher addiction rates, fewer quit attempts, and more relapses [14,15], approaching them via Internet may have added potential.