Smoking has several harmful effects on our health and affects our organs, leading to the incidence of many life-threatening diseases. Furthermore, it is one of the most preventable causes of death. Despite its detrimental effect on our health, quitting smoking is challenging due to the tobacco addictive chemicals and humans’ psychological dependency on it. Nonetheless, there are different approaches to support people willing to stop smoking. One method is eHealth computer tailoring, which helps personalize feedback given to smokers based on psychological models of behavioral change based on pre-defined if-then-else rules. These methods showed to generate positive results in terms of high abstinence rates and cost-effectiveness. However, new innovative solutions are available to improve the eHealth methods for smoking cessation further. One of those methods is related to recommender systems technology. Recommender systems are AI algorithms that can select the most relevant item (such as a piece of text, book, movie, or product) from a set of items for each user. Depending on the type of recommender system, relevance is determined considering different methods and variables. A commonly used method for calculating relevance is the “collective intelligence” approach. This approach uses algorithms to generate a user profile for each user (e.g., using demographic variables) and calculate how relevant a specific item is based on the given relevance of that item for users with similar user profiles.These systems can learn from user feedback over time in that the users rate the relevance of the recommended items, which helps train the system for making future recommendations. For decades, the scientific community has explored the relevance of these systems in other fields such as leisure (movie recommendations on Netflix) and e-commerce (product recommendations on Amazon). Due to their potential and proven effectiveness in other fields but limited application in the healthcare sector, which began onlya few years ago, studying how these systems can be applied for smoking cessation is crucial.In Chapter 2 of this dissertation, we have conducted a scoping review to assess the existing knowledge and research gaps using recommender systems in healthcare,also known as health recommender systems (HRSs). We assessed their technical and healthcare aspects through this review. Based on its results, we then generated a new taxonomy for these types of systems. Next, we provided a detailed description of a health recommender system (HRS) design process with collective intelligence grounded in behavioral science for smoking cessation using the I-Change model as an example. In Chapter 3, we explained all the steps and the system design, including algorithm components, messages creation, and user interface design, to help interested stakeholders better understand such systems, which would provide inspiration and a basis for future studies. Furthermore, we performed an assessment study to test the created HRS using collective intelligence in a real-world setting with a follow-up period of six months. The control condition was a simpler version of the created HRS in this assessment, except for the collective intelligence component. In Chapter 4, we reported the protocolof this study and analyzed the actual results regarding the appreciation, engagement, dropouts, and smoking abstinence generated by the system (Chapter 5). Chapter 1 provides a general introduction to the problem associated with smoking cessation. First, it introduces different existing support approaches, focusing on the ones related to behavioral change and their application in computer-tailored interventions. Then, it presents the recommender system technology and its different types that exist as an option for facilitating computer-tailored interventions. Further, it highlights the appreciation and engagement metrics, which are the factors that complement abstinence for intervention success. Chapter 2 contains a scoping review that provides an analysis of the state-of-the-art HRS, identifying the research gaps and the elements that should be improved when applying this technology to the healthcare sector. From this study, we identified that the collaborative filtering technique was the most-used information filtering method. However, it was also observed that there is a lack of applying behavioral change theories and factors in HRS studies. Furthermore, these studies neither implemented the principles of tailoring nor assessed their (cost)-effectiveness. Therefore, a taxonomy was proposed to facilitate consistent classification and better comprehension of these systems. This taxonomy included the domain of the study (e.g., the type of population, country, therapeutic area), the methodology and procedures of the study (the duration, number of users, outcomes), health behavior change factors (e.g., self-efficacy, social influence, attitudes), and the technical aspects required to understand the algorithm (e.g., recommendation technology, profile generation techniques).Chapter 3 provides a multidisciplinary and comprehensive description of the design process of an HRS for supporting smoking cessation that uses collective intelligence in combination with the I-Change behavioral change model. This detailed description contributed to help reveal the process of how an HRS can be built to support behavioral change interventions. This process had not been disclosed in detail before, and this lack of transparency can act as a barrier for behavioral change researchers in using HSR technology. The new system was built based on a previous HRS that utilized a mobile app to support smokers trying to stay abstinent by sending them motivational messages. First, we identified the areas that needed improvements based on the app’s usage data. Then, we implemented relevant changes to our new system design (e.g., increasing the granularity of the possible user feedback from three options to five options). Our final mobile app was supposed to be more streamlined and usable thanthe first version. The generated HRS was a hybrid algorithm with a knowledge-based step and a collaborative-filtering step in cascade. It used 58 variables to compute the similarity formula for choosing recommendations; from the total, 47 were related to the determinants of the I-Change model. Altogether, 331 motivational messages were created, and ten different health communication methods were considered for their design. Chapter 4 explains the protocol to be followed to assess the system created in Chapter 3. This protocol included the description of a clinical pilot and a public pilot. We used the latter one to analyze the HRS in this dissertation. Chapter 5 presents, discusses and reflects on the results obtained from the public pilot. The public pilot was a double-blinded experiment. Those smokers who can read English or Mandarin and download a mobile app from the Internet were eligible to participate. After creating their account and answering questions relevant to their user profile (e.g., name, age, gender, level of addiction, and motivation to quit), they can set a quitting day to start receiving personalized motivational text messages via the mobile app. Smokers were randomly allocated to the group where such messages were generated by the new HRS, which was described in Chapter 3, or to the group associated with a simpler version of the algorithm, without collective intelligence (using only the knowledge-based step), selected and sent these messages. A total of 371 participants were eligible to be part of the study analysis. Smokers were followed up for six months, starting from their quitting day, and were asked weekly about their smoking abstinence through a voluntary question in the app.Moreover, we measured their message appreciation and engagement. The attributes (factors) considered as possible indicators of differences in the study outcomes included the motivation to quit, nicotine dependence, age, gender, and completion of the extended user profile questionnaire. They were studied as potential covariates in the statistical analysis. No statistically significant differences were found neither for the analysis on available data of the 7D-PP abstinence averaging the abstinence reports across the study nor for the penalized imputation analysisof both the 7D-PP abstinence averaging the abstinence reports across the study and the 7D-PP considering only the last available abstinence report. However, the analysis on available data for the 7D-PP considering only the last available abstinence reportshowed lower abstinence rates in the HRS using collective intelligence. Also, the results showed that the HRS using collective intelligence did not have statistically significant differences for message appreciation, number of rated messages, and number of quitting attempts. However, the collective intelligence algorithm performed worse regarding the number of abstinence reports and active days. The sub-group analysis showed that the completion of the extended user profile did significantly impact the engagement of the participants reducing the number of dropouts in both groups and increasing the number of quitting attempts in participants who received messages selected with the collective intelligence. Finally, Chapter 6 provides a general discussion of the main findings and conclusions of all the studies presented in this dissertation (from chapters 2–5). It also contains the main methodological considerations for this dissertation, such as the strengths and limitations, risks, reflections for practice, and the impact of this thesis on the scientific community. In conclusion, the studies presented in this dissertation showed that although HRSs are gaining traction in the healthcare sector, they are still novel, with underreported details and suboptimal application, as they do not take advantage of the behavioral change theories. However, we have shown that they can be used as an alternative approach to traditional tailoring for behavioral interventions by embedding behavioral science in the design of theseemergent systems. We compared the HRSs with and without collective intelligence technology for a trial for smoking cessation, measuring their performance in real-life conditions. The results showed that despite showing some positive results in terms of engagement –number of quitting attempts -when completing the extended user profile, the HRS using collective intelligence did not manage to improve smoking behavior, appreciation, and engagement compared to the other HRS. In addition, some of the engagement and abstinence metrics led to worse results. Furthermore, although we achieved better smoking cessation outcomes than quitting cold turkey or with brief clinician advice, our HRS did not improve the abstinence rates achieved by other approaches in smoking cessation, such as traditional computer tailoring. Further, it is still unclear why the theoretical potential of collective intelligence did not provide the expected benefits in our study. Therefore, future research is needed to find out how HRS-based interventions, using or not using the collective intelligence technology, can be improved to achieve better outcomes in terms of behavioral change.