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This chapter illustrates a previously discussed pragmatic methodology to design a tailored smoking cessation digital health program. Guided by principles of intervention planning protocols, sociocognitive theories, such as the Integrated-Change Model, are useful to design evidence-based interventions. Intervention planning protocols can be deemed highly complex and resources may be limited. The pragmatic approach discussed to design digital health simplifies this process in a four-step approach using smoking cessation as an example. Tobacco smoking remains the worldwide leading cause of death, illness, and impoverishment. Research on this topic to develop and validate tailored Digital health (dHealth) interventions is highly relevant for society. The specific change objectives can be planned based on the determinants of the Integrated-Change Model.When designing an intervention that covers any of the behavior change phases (i.e., awareness, motivation, action) , it is needed to identify the most relevant determinants and related beliefs of each phase. This identification entails looking which determinants and related beliefs predict the transition from smoking to nonsmoking. This is usually done by combining a qualitative with a quantitative exercise. Once we know which determinants and beliefs are most relevant for our target group, we can select these items to develop the program content (i.e., messages) and the algorithm to select and send the personalized contents. At this point, choices need to be made concerning how many intervention interactions (sessions) will be useful, realistic, and feasible.Research is needed to progress the efficacy of tailored dHealth. One approach that is especially interesting is the use of artificial intelligence to innovate the algorithms of tailoring, moving from rule-based to data-driven tailored dHealth. Using validated solutions and working with a multidisciplinary team of experts to be able to understand clinical, psychological, and artificial intelligence areas is of utmost importance to achieve safe and satisfactory results combining AI and tailoring to induce behavioral changes.

This chapter illustrates a previously discussed pragmatic methodology to design a tailored smoking cessation digital health program. Guided by principles of intervention planning protocols, sociocognitive theories, such as the Integrated-Change Model, are useful to design evidence-based interventions. Intervention planning protocols can be deemed highly complex and resources may be limited. The pragmatic approach discussed to design digital health simplifies this process in a four-step approach using smoking cessation as an example. Tobacco smoking remains the worldwide leading cause of death, illness, and impoverishment. Research on this topic to develop and validate tailored Digital health (dHealth) interventions is highly relevant for society. The specific change objectives can be planned based on the determinants of the Integrated-Change Model.When designing an intervention that covers any of the behavior change phases (i.e., awareness, motivation, action) , it is needed to identify the most relevant determinants and related beliefs of each phase. This identification entails looking which determinants and related beliefs predict the transition from smoking to nonsmoking. This is usually done by combining a qualitative with a quantitative exercise. Once we know which determinants and beliefs are most relevant for our target group, we can select these items to develop the program content (i.e., messages) and the algorithm to select and send the personalized contents. At this point, choices need to be made concerning how many intervention interactions (sessions) will be useful, realistic, and feasible.Research is needed to progress the efficacy of tailored dHealth. One approach that is especially interesting is the use of artificial intelligence to innovate the algorithms of tailoring, moving from rule-based to data-driven tailored dHealth. Using validated solutions and working with a multidisciplinary team of experts to be able to understand clinical, psychological, and artificial intelligence areas is of utmost importance to achieve safe and satisfactory results combining AI and tailoring to induce behavioral changes.

Background: Recommender systems are information retrieval systems that provide users with relevant items (e.g., through messages). Despite their extensive use in the e-commerce and leisure domains, their application in healthcare is still in its infancy. These systems may be used to create tailored health interventions, thus reducing the cost of healthcare and fostering a healthier lifestyle in the population. Objective: This paper identifies, categorizes, and analyzes the existing knowledge in terms of the literature published over the past 10 years on the use of health recommender systems for patient interventions. The aim of this study is to understand the scientific evidence generated about health recommender systems, to identify any gaps in this field to achieve the United Nations Sustainable Development Goal 3 (SDG3) (namely, “Ensure healthy lives and promote well-being for all at all ages”), and to suggest possible reasons for these gaps as well as to propose some solutions. Methods: We conducted a scoping review, which consisted of a keyword search of the literature related to health recommender systems for patients in the following databases: ScienceDirect, PsycInfo, Association for Computing Machinery, IEEExplore, and Pubmed. Further, we limited our search to consider only English-lan-guage journal articles published in the last 10 years. The reviewing process comprised three researchers who filtered the results simultaneously. The quantitative synthesis was conducted in parallel by two researchers, who classified each paper in terms of four aspects—the domain, the methodological and procedural aspects, the health promotion theoretical factors and behavior change theories, and the technical aspects—using a new multidisciplinary taxonomy. Results: Nineteen papers met the inclusion criteria and were included in the data analysis, for which thirty-three features were assessed. The nine features associated with the health promotion theoretical factors and behavior change theories were not observed in any of the selected studies, did not use principles of tailoring, and did not assess (cost)-effectiveness. Discussion: Health recommender systems may be further improved by using relevant behavior change strategies and by implementing essential characteristics of tailored interventions. In addition, many of the features required to assess each of the domain aspects, the methodological and procedural aspects, and technical aspects were not reported in the studies. Conclusions: The studies analyzed presented few evidence in support of the positive effects of using health recommender systems in terms of cost-effectiveness and patient health outcomes. This is why future studies should ensure that all the proposed features are covered in our multidisciplinary taxonomy, including integration with electronic health records and the incorporation of health promotion theoretical factors and behavior change theories. This will render those studies more useful for policymakers since they will cover all aspects needed to determine their impact toward meeting SDG3. European Union's Horizon 2020 No 681120