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BackgroundGrowth hormone deficiency (GHD) is a rare disorder characterized by inadequate secretion of growth hormone (GH) from the anterior pituitary gland. One of the challenges in optimizing GH therapy is improving adherence. Using digital interventions may overcome barriers to optimum treatment delivery. Massive open online courses (MOOCs), first introduced in 2008, are courses made available over the internet without charge to a large number of people. Here, we describe a MOOC aiming to improve digital health literacy among healthcare professionals managing patients with GHD. Based on pre- and post-course assessments, we evaluate the improvement in participants’ knowledge upon completion of the MOOC.MethodsThe MOOC entitled ‘Telemedicine: Tools to Support Growth Disorders in a Post-COVID Era’ was launched in 2021. It was designed to cover 4 weeks of online learning with an expected commitment of 2 h per week, and with two courses running per year. Learners’ knowledge was assessed using pre- and post-course surveys via the FutureLearn platform.ResultsOut of 219 learners enrolled in the MOOC, 31 completed both the pre- and post-course assessments. Of the evaluated learners, 74% showed improved scores in the post-course assessment, resulting in a mean score increase of 21.3%. No learner achieved 100% in the pre-course assessment, compared with 12 learners (40%) who achieved 100% in the post-course assessment. The highest score increase comparing the pre- and the post-course assessments was 40%, observed in 16% of learners. There was a statistically significant improvement in post-course assessment scores from 58.1 ± 18.9% to 72.6 ± 22.4% reflecting an improvement of 14.5% (p ConclusionThis “first-of-its-kind” MOOC can improve digital health literacy in the management of growth disorders. This is a crucial step toward improving the digital capability and confidence of healthcare providers and users, and to prepare them for the technological innovations in the field of growth disorders and growth hormone therapy, with the aim of improving patient care and experience. MOOCs provide an innovative, scalable and ubiquitous solution to train large numbers of healthcare professionals in limited resource settings.

Accurately measuring sleep and its quality with polysomnography (PSG) is an expensive task. Actigraphy, an alternative, has been proven cheap and relatively accurate. However, the largest experiments conducted to date, have had only hundreds of participants. In this work, we processed the data of the recently published Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study to have both PSG and actigraphy data synchronized. We propose the adoption of this publicly available large dataset, which is at least one order of magnitude larger than any other dataset, to systematically compare existing methods for the detection of sleep-wake stages, thus fostering the creation of new algorithms. We also implemented and compared state-of-the-art methods to score sleep-wake stages, which range from the widely used traditional algorithms to recent machine learning approaches. We identified among the traditional algorithms, two approaches that perform better than the algorithm implemented by the actigraphy device used in the MESA Sleep experiments. The performance, in regards to accuracy and F1 score of the machine learning algorithms, was also superior to the device’s native algorithm and comparable to human annotation. Future research in developing new sleep-wake scoring algorithms, in particular, machine learning approaches, will be highly facilitated by the cohort used here. We exemplify this potential by showing that two particular deep-learning architectures, CNN and LSTM, among the many recently created, can achieve accuracy scores significantly higher than other methods for the same tasks.Other Information Published in: npj Digital Medicine License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1038/s41746-019-0126-9

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.

European Commission Horizon 2020 Science Foundation Ireland Insight Research Centre Qatar National Library

World Health Organization (WHO) Dataset. (DOCX 31 kb)

Informed consent. (DOCX 39 kb)

Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 Checklist: recommended items to address in a clinical trial protocol and related documents*. (DOC 120 kb)

International audience; Objective: To summarize the state of the art during the year 2016 in the areas related to consumer health informatics and education with a special emphasis in secondary use of patient data. Methods: We conducted a systematic review of articles published in 2016, using PubMed with a predefined set of queries. We identified over 320 potential articles for review. Papers were considered according to their relevance for the topic of the section. Using consensus, we selected the 15 most representative papers, which were submitted to external reviewers for full review and scoring. Based on the scoring and quality criteria, five papers were finally selected as best papers Results: The five best papers can be grouped in two major areas: 1) methods and tools to identify and collect formal requirements for secondary use of data, and 2) innovative topics highlighting the interest of carrying on "secondary" studies on patient data, more specifically on the data self-expressed by patients through social media tools. Regarding the formal requirements about informed consent, the selected papers report a comparison of legal aspects in European countries to find a common and unified grammar around the concept of "data donation". Regarding innovative approaches to value patient data, the selected papers report machine learning algorithms to extract knowledge from patient experience and satisfaction with health care delivery, drug and medication use, treatment compliance and barriers during cancer disease, or acceptation of public health actions such as vaccination. Conclusions: Secondary use of patient data (apart from personal health care record data) can be expressed according to many ways. Requirements to allow this secondary use have to be harmonized between countries, and social media platforms can be efficiently used to explore and create knowledge on patient experience with health problems or activities. Machine learning algorithms can explore those massive amounts of data to support health care professionals, and institutions provide more accurate knowledge about use and usage, behaviour, sentiment, or satisfaction about health care delivery. Keywords Consumer health informatics; personal health information; social media; secondary data use; internet Yearb Med Inform 2017:172-7 http://dx.

Background: Facebook, the most popular social network with over one billion daily users, provides rich opportunities for its use in the health domain. Though much of Facebook’s data are not available to outsiders, the company provides a tool for estimating the audience of Facebook advertisements, which includes aggregated information on the demographics and interests, such as weight loss or dieting, of Facebook users. This paper explores the potential uses of Facebook ad audience estimates for eHealth by studying the following: (1) for what type of health conditions prevalence estimates can be obtained via social media and (2) what type of marker interests are useful in obtaining such estimates, which can then be used for recruitment within online health interventions. Objective: The objective of this study was to understand the limitations and capabilities of using Facebook ad audience estimates for public health monitoring and as a recruitment tool for eHealth interventions. Methods: We use the Facebook Marketing application programming interface to correlate estimated sizes of audiences having health-related interests with public health data. Using several study cases, we identify both potential benefits and challenges in using this tool. Results: We find several limitations in using Facebook ad audience estimates, for example, using placebo interest estimates to control for background level of user activity on the platform. Some Facebook interests such as plus-size clothing show encouraging levels of correlation (r=.74) across the 50 US states; however, we also sometimes find substantial correlations with the placebo interests such as r=.68 between interest in Technology and Obesity prevalence. Furthermore, we find demographic-specific peculiarities in the interests on health-related topics. Conclusions: Facebook’s advertising platform provides aggregate data for more than 190 million US adults. We show how disease-specific marker interests can be used to model prevalence rates in a simple and intuitive manner. However, we also illustrate that building effective marker interests involves some trial-and-error, as many details about Facebook’s black box remain opaque.

BackgroundThe importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. ObjectiveThe objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. MethodsActigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). ResultsDeep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional logistic regression. “CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional logistic regression (0.6463). ConclusionsDeep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep.

In this article, we describe the design of two recommender systems (RS) designed to support the smoking cessation process through a mobile application. We plan to use a hybrid RS (content-based, utility-based, and demographic filtering) to tailor health recommendation messages, and a content-based RS to schedule a timely delivery of the message. We also define metrics that we will use to assess their performance, helping people quit smoking when we run the pilot., Comment: Please, cite as: Hors-Fraile, S., N\'u\~nez Benjumea, F.J., Carrasco Hern\'andez, L., Ruiz, F.O., Fernandez-Luque, L. (2016) Design of two combined health recommender systems for tailoring messages in a smoking cessation app. International Workshop on Engendering Health with RecSys co-located with ACM RecSys 2016. Boston, MA, USA

The importance of sleep is paramount for maintaining physical, emotional and mental wellbeing. Though the relationship between sleep and physical activity is known to be important, it is not yet fully understood. The explosion in popularity of actigraphy and wearable devices, provides a unique opportunity to understand this relationship. Leveraging this information source requires new tools to be developed to facilitate data-driven research for sleep and activity patient-recommendations. In this paper we explore the use of deep learning to build sleep quality prediction models based on actigraphy data. We first use deep learning as a pure model building device by performing human activity recognition (HAR) on raw sensor data, and using deep learning to build sleep prediction models. We compare the deep learning models with those build using classical approaches, i.e. logistic regression, support vector machines, random forest and adaboost. Secondly, we employ the advantage of deep learning with its ability to handle high dimensional datasets. We explore several deep learning models on the raw wearable sensor output without performing HAR or any other feature extraction. Our results show that using a convolutional neural network on the raw wearables output improves the predictive value of sleep quality from physical activity, by an additional 8% compared to state-of-the-art non-deep learning approaches, which itself shows a 15% improvement over current practice. Moreover, utilizing deep learning on raw data eliminates the need for data pre-processing and simplifies the overall workflow to analyze actigraphy data for sleep and physical activity research.

Background: As cancer survival rates increase, the challenge of ensuring that cancer survivors reclaim their quality of life (QoL) becomes more important. This paper outlines the research element of a research and training program that is designed to do just that. Objective: Bridging sectors, disciplines, and geographies, it brings together eight PhD projects and students from across Europe to identify the underlying barriers, test different technology-enabled rehabilitative approaches, propose a model to optimize the patient pathways, and examine the business models that might underpin a sustainable approach to cancer survivor reintegration using technology. Methods: The program, funded under the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 722012, includes deep disciplinary PhD projects, intersectoral and international secondments, interdisciplinary plenary training schools, and virtual subject-specific education modules. Results: The 8 students have now been recruited and are at the early stages of their projects. Conclusions: CATCH will provide a comprehensive training and research program by embracing all key elements—technical, social, and economic sciences—required to produce researchers and project outcomes that are capable of meeting existing and future needs in cancer rehabilitation. [JMIR Res Protoc 2018;7(1):e14]

Self-regulated training logbook. Preview of one page of the pro forma logbook distributed to every study participant. The patient should adequately fill one page for each working day, according to the performed self-regulated training. (DOCX 24 kb)

Conventional self-regulated exercise programs. Illustrations and written instructions for each self-regulated exercise. A trained physiotherapist teaches the customized program and a printed handout is given to the patient. (DOCX 1587 kb)

Published version, also available at http://dx.doi.org/10.1186/s12911-015-0143-9 Trial registration: The trial is registered at clinicaltrials.org under the identifier ClinicalTrials.gov NCT:02128620 Background: Chlamydia is the most common reportable sexually transmitted disease (STD) in Norway, and its incidence in the two northernmost counties has been disclosed to be nearly the double of the Norwegian average. The latest publicly available rates showed that 85.6% of the new cases were diagnosed in people under 29 years old. The information and communication technologies are among the most powerful influences in the lives of young people. The Internet can potentially represent a way to educate on sexual health and encourage young people, and especially youth, to be tested for STDs. If hospital websites include an easy and anonymous system for scheduling appointments with the clinic, it is possible that this could lead to an increase in the number of people tested for STDs. Methods: The purpose of the study is to assess the impact of a game-based appointment system on the frequency of consultations at a venereology unit and on the use of an educational web app. An A/B testing methodology is used. Users from the city of Tromsø, in North Norway, will be randomized to one of the two versions of the game-style web app on sexual health at www.sjekkdeg.no. Group A will have access to educational content only, while group B will have, in addition, access to a game-based appointment system with automatic prioritization. After one year of the trial, it will be analyzed if the game-based appointment system increases the number of consultations at the venereology unit and if health professionals deem the system useful. Discussion: This study will explore if facilitating the access to health services for youth through the use of a game-based appointment system integrated in a game-style web app on sexual health education can have an impact on appointment rates.

Papers number RQ1_2, RQ1_3, RQ3_2 and RQ4_1 of the thesis are not available in Munin. RQ1_2: Fernandez-Luque L, Elahi N, Grajales FJ 3rd.: 'An analysis of personal medical information disclosed in YouTube videos created by patients with multiple sclerosis'. Stud Health Technol Inform. 2009;150:292-6. Available at: http://dx.doi.org/10.3233/978-1-60750-044-5-292 RQ1_3: S Konstantinidis, L Fernandez-Luque, P Bamidis, R Karlsen: 'The Role of Taxonomies in Social Media and the Semantic Web for Health Education'. Methods Inf Med 2013; 52. Available at: http://dx.doi.org/10.3414/ME12-02-0005 RQ3_2: Chomutare T, Arsand E, Fernandez-Luque L, Lauritzen J, Hartvigsen G.: 'Inferring community structure in healthcare forums'. An empirical study. Methods Inf Med. 2013;52(2):160-7. Epub 2013 Feb 8. Available at: http://dx.doi.org/10.3414/ME12-02-0003 RQ4_1: Fernandez-Luque L, Karlsen R, Melton GB.: 'HealthTrust: Trust-based Retrieval of YouTube's Diabetes Channels', 2011, 20th ACM international conference on Information and knowledge management. Available at: http://dx.doi.org/10.1145/2063576.2063854 There is a global trend towards the use of the Internet to search for information about health issues. We have access to a wide range of online health information; especially the so-called social media (e.g. blogs, videos). However, finding good quality resources is not easy in the current context of information overload. Today, very relevant and valuable health social media has to compete in visibility with misleading information such as anti-vaccination and pro-anorexia content. General web information retrieval approaches, such as Google, tend to retrieve popular content that can be misleading or even repulsive. For example, people searching for videos about diabetes foot care will discover that the top videos retrieved by a YouTube search include macabre amputations. Traditional health information retrieval approaches based on quality labels face many scalability challenges. The PhD project described here focuses on the unmet need for better technical solutions for the retrieval of high quality and relevant health social media. This thesis summarizes nearly six years’ work in the field of health social media summarized in ten research papers. I have applied a wide range of research methods such as qualitative research with patients, web-data analysis and literature reviews. My first research challenge was to grasp some understanding of the emerging health social media avalanche where research literature was virtually nonexistent. Secondly, I explored a wide range of technical solutions for the retrieval of relevant and trustworthy health information such as web search-engines, recommender systems and personalized health education systems. Building on the knowledge acquired during the dissertation, I proposed a new trust-based metric called HealthTrust for the retrieval of health social media. HealthTrust is a metric measuring the trustworthiness of the content within a health community and it can be used to rank search results of health social media. The rationale for choosing an approach based on social network analysis within a health community relies on the assumption that health communities have a common shared knowledge about the relevance and trustworthiness of the content and their providers. The HealthTrust algorithm was successfully tested for the retrieval of diabetes social videos.

Informed consent. (DOCX 39 kb)

CGIN Press Release - May 29th 2015. (PDF 283 kb)

CGIN Press Release - May 29th 2015. (PDF 283 kb)

World Health Organization (WHO) Dataset. (DOCX 31 kb)

Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 Checklist: recommended items to address in a clinical trial protocol and related documents*. (DOC 120 kb)

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.