The use of chatbots in eHealth applications has grown significantly, providing an interactive platform for users to improve their health and lifestyle. To enhance these applications, human feedback has been incorporated, with a coach offering personalized guidance to help users achieve their goals. However, deciding whether to provide human feedback or rely solely on automated systems remains a challenge, especially given the cost constraints associated with human involvement. This study presents a reinforcement learning (RL) model designed to optimize this decision by analyzing users’ states and predicting the potential benefit of such interventions. The model was trained on data from a longitudinal study involving over 500 daily smokers and vapers who interacted with a virtual coach across multiple sessions. Our findings indicate that the RL model effectively assists in determining whether human feedback is valuable, increasing the likelihood of behavior change, enhancing users’ quitter identities, and further reducing smoking/vaping frequency. This research contributes to the development of more effective, resource-efficient eHealth interventions.

This study analyzed the self-introductions of participants in a smoking and vaping cessation program to understand their motivations, barriers, and support needs. Through thematic analysis of 787 participant introductions, six main themes were identified: Motivations for Quitting, Previous Attempts to Quit, Barriers to Quitting, Desired Support, Usage Patterns, and Identity. Health concerns emerged as the most common motivation for quitting, while psychological and social challenges were the primary barriers. The study found no significant correlation between the length of introductions and participant engagement or satisfaction, suggesting that while introductions provide valuable qualitative insights, they do not relate to other factors. The findings describe the contents of introductions to human coaches, although the direct impact on outcomes requires further exploration. Future research should employ a controlled design to evaluate the effectiveness of detailed participant introductions in enhancing eHealth support and consider the complexity of dual usage in smoking and vaping cessation efforts.

Smoking and vaping cessation remains a significant public health challenge despite the availability of numerous aids and eHealth applications. This study explores the reasons behind users' preference for human feedback when preparing to quit smoking or vaping, aiming to address a gap in existing literature on the integration of human elements in eHealth platforms. The research involved 479 participants interacting with a virtual coach, with some receiving human feedback. We conducted a thematic analysis of two open-text questions with 265 responses each from post-questionnaires, and key themes such as emotional connection, personalized advice, effectiveness, motivation, and accountability were identified. Through using quantitative data and previously published research, these findings were further explained. The results from the quantitative analysis show that incorporating human elements in eHealth applications can enhance smoking cessation support. This research provides insights into the main reasons how the human support in eHealth applications should be designed. Key recommendations include designing human feedback to offer empathy and validation, tailoring feedback to individual needs, incorporating interactive elements to maintain engagement, providing constant encouragement, and establishing accountability mechanisms.

Smoking remains one of the largest health concerns worldwide, which is why eHealth applications with virtual coaches have been developed to assist smokers with quitting. Providing additional feedback from human coaches during such smoking cessation programs can further improve the effectiveness of the intervention. However, due to budgetary constraints and the limited availability of human coaches, it is important to make informed decisions about when someone gets human support to optimize the effectiveness. This research investigates the use of reinforcement learning (RL) to determine when to provide human feedback in quitting smoking with a virtual coach. Using data from a longitudinal study, we implemented an RL model that decides when to involve a human coach based on users' appreciation for human support and their self-efficacy, optimizing the effort that people spend on preparatory activities and their likelihood of returning to the program. Results show that the model is effective in allocating human support, increasing users' effort and return likelihood while considering the cost of human coaches. These findings support using RL to help with determining when to provide human support in smoking cessation programs.

Background. Quitting smoking is a challenge nowadays. Virtual coaches offer autonomous, personalized guidance for smoking cessation. However, such systems cannot replace human coaches completely. In situations, when human coaches cannot provide help to everyone - a virtual coach could follow a set of ethical principles to decide on who should get the feedback from a human.
Objective. Our study aims to identify users’ preferences on ethical principles that a virtual coach should follow to decide when to allocate human feedback to individuals preparing to quit smoking.
Methods. Our research was based on pre-gathered data, that included participants’ responses to open and closed questions regarding feedback allocation principles. Thematic analysis was conducted on these responses. Triangulation was performed using a qualitative literature review and quantitative data analysis.
Results. Four main themes were identified: (1) Struggling the Most (63.75%), (2) Increasing Chances of Success the Most (13.75%), (3) Equal Treatment (11.25%), and (4) Appreciating the Most (11.25%). Participants prioritized support for those experiencing the greatest difficulty in smoking cessation. The triangulation supported the validity of these themes.
Conclusions. Our study highlights the importance of integrating user-preferred ethical principles in virtual coaching systems for smoking cessation. Prioritization of users who struggle the most can increase the effectiveness and fairness of such systems, potentially increasing success rates. Future research should explore additional ethical principles, combining several principles into systems, and real-world application of these findings to further refine virtual coaching in healthcare.

To assist smokers in potentially quitting their habit, this paper investigates digital eHealth applications. Based on a dataset provided by research into an eHealth application, it aims to determine if persuasive activities can convince users of the usefulness of competencies determined to assist in quitting smoking. A thematic analysis was used on the participant's qualitative responses to the activities. Using this and the quantitative data derived, insights into the efficacy of persuasion were determined.
It was determined that engagement with the optional qualitative aspect of the data produced similar utility perspectives on the competencies to those who did not comment. It was noted that the general perspective of the competencies rose after completing the activity, however not to a significant degree. Additionally, no notable correlations between age, gender or educational level and increased perception of the competency arose. Several interesting remarks from participants were analysed to offer considerations for any future research in this field.

Smoking has been one of the great threats to health in recent years, being strongly correlated with multiple negative health consequences, including lung cancer. Recent research suggests that artificial intelligence chatbots can be effective in persuading healthy behavior change. However, these chatbots usually rely on persuasive techniques to achieve their goal. Such techniques depend on identifying and meeting users' needs to be effective. To help improve understanding of the domain of healthy behavior change, we proposed a study which analysed the needs of daily smokers as they emerged from their interactions with a chatbot specifically designed to help them quit. The study performed a thematic analysis on users' free-text feedback, from which a set of 8 themes that directly correspond to 8 different needs were observed. The user needs were correlated with their genders, ages and highest completed education levels. While most of the results indicate that there is no significant correlation between the needs and user characteristics, which suggests that user needs are evenly distributed, certain correlations were highlighted for further analysis.

Unhealthy behaviors such as smoking is the major cause for premature deaths and changing behaviors by oneself can be difficult. That is where eHealth applications come into rescue. One of the recent research explored the possibility of using a Reinforcement Learning model to choose persuasive types for a virtual coach to adopt to persuade people to prepare for smoke-quitting and it has shown advantages. However, there are still more aspects to investigate in this context except the persuasive types of the messages, and this paper intended to further look into using reinforcement learning to choose activities for preparing the users to quit smoking, To be more specific, we implemented and evaluated a reinforcement learning model to choose activities to optimize both the effort spent by the users and also the likelihood of them staying for the next session. The result suggests that reinforcement learning is a promising approach to choose activities for people to prepare for quitting smoking and it can move the users to states that they are more likely to spend a good effort on the activities and are more likely to come back to the next session.

Humans interact with various Artificial Social Agents (ASAs) on a daily basis. ASAs range from the Honda robot ASIMO to Apple’s Siri. To measure the perception of human-ASA interactions, a standardized questionnaire was created. Yet, this questionnaire was so far only available in English and Chinese. It has been found that culture can affect how these interactions are perceived. The aim of this study is to answer the question: What are the differences and similarities of the English and German human-ASA interaction interpretations? In this paper, we translate the questionnaire into German, validate it. Once proven valid, we give the English and German questionnaire on bilingual participants who watch a human-ASA interaction video and rate it in both languages. We measure the differences and similarities between the English and German responses. At the end, we combine the finding from the questionnaire results with examples from literature to form recommendations for future ASA developments. We conclude that an average good level of correlation between the two languages for the 90 questionnaire items (ICC M = 0.65, SD = 0.14, range [0.27, 0.90]), on the construct level (ICC M = 0.8, SD = 0.1, range [0.51, 0.92]), and for the 24 representative items (M = 0.67, SD = 0.14, range[0.31, 0.90]). Additionally, we found systematic differences between the English questionnaire scores of the bilingual sample seen in this study and a previously established mixed-English sample.

The Artificial Social Agent(ASA) questionnaire serves as a tool to assess the interaction between humans and ASA\cite{OGQuestionnaire}. In an effort to increase the number of people who can make use of the questionnaire, previous work has been done to translate this questionnaire to Mandarin Chinese\cite{Chinese}. To increase the number of people who can use this questionnaire even more, we have translated it to German using a similar iterative translating process. We had professionals translate the questionnaire and ran 3 rounds of surveys to see what questions needed improvement. Then, there was a final survey that evaluated the German version to see how well it performs and to see how it compares to the English version. In order to evaluate the cultural differences between the Chinese and German translations, the data of both translation evaluations were compared. From these results, it was shown that ASA developers who use the German version of the ASA questionnaire can make comparisons on a construct level to the English version. This was, however, only possible when using the full questionnaire, not the short version. Additionally, we have found that there are differences in how Mandarin Chinese speakers and German speakers rate ASAs.

Artificial social agents (ASAs) are systems designed to interact with humans in a socially intelligent manner. As the field of robotics is rapidly advancing, some studies focused on creating more effective agents by analysing how people perceive them. However, culture affects people’s perception of ASAs. So, cultural aspects must be examined in order to create more effective ASAs. This study aims to contribute to the research of cultural influence on ASAs by discovering the cultural similarities and differences in the perception of ASAs from Dutch and Chinese speakers. An English questionnaire has been made to evaluate ASAs and also has been translated into Chinese. In this study, the questionnaire is translated into Dutch and validated using both the original and the translated questionnaire. While assessing the translation, data is gathered from Dutch speakers on a human-ASA interaction. This data is then compared to the previously collected data from the Chinese translation study by calculating their mean, standard deviation and t-distribution of the differences between both cultural groups. On an item level, the results show a satisfactory level of correlation (ICC M = 0.67, SD = 0.12, range [0.36, 0.92]). On a construct/dimension level, results show a good level of correlation (ICC M = 0.80, SD = 0.11, range [0.52, 0.93]). Correction values for the translation have been advised for converting item scores between the two questionnaires. Cultural differences have been found and reported between Dutch and Chinese speakers, which could be used in future research on creating more effective ASAs.

Physical activity is one of the main factors that contribute to reducing the chance of chronic diseases such as cardiovascular disease, obesity, and depression, all while improving an individual’s health in general. While this is the case, the fact still remains that many adults across the world do not reach the minimum recommendations for physical activity. Setting physical activity goals is one of the most common approaches found in both self-help fitness apps, or medical interventions for increasing physical activity. The issue is that goal setting on its own cannot help people become more physically active, since, if they are not committed to reaching a goal, a person will just abandon it instead. Recent literature shows that creating plans for when to perform physical activity has the potential to help people commit to reaching their goal. Being committed to following the plan is therefore important for ultimately reaching the goal, but only creating the plan is not enough, since people might abandon it. Furthermore, dyadic planning, in which a helper aids the person in creating the plan, has produced even better results than individual planning. Thus, the aim of this work is to develop a virtual coach, which plays the role of the helper in dyadic planning and motivates people to commit themselves to following the plan, so that they can reach their physical activity goals. To facilitate this process, the virtual coach, named Jamie, operates based on reinforcement learning, giving it the ability to select the best persuasive strategy to use. It does so by taking into account the person's situation (opinions of the plan and of planning in general), as well as how these opinions might change based on what persuasive strategy the agent chooses to employ. The persuasive strategies considered were: proposing to make changes to the plan, explaining why planning is useful, identifying and dealing with barriers, and showing testimonials from other people who created plans and used them to reach their goals. Through an observational study, data for the reinforcement learning model was gathered, and a model was trained on the data. Analysing the data revealed that the choice of persuasive strategy is not crucial, as all of them had similar effects on the person's situation, and these effects were small to moderate. At the same time, we saw moderate differences when comparing the situation at the beginning and end of the conversation, indicating that the combined effect of multiple persuasive strategies is needed to change a person's situation. We also investigated the effect of including or excluding the person's situation from the model. If the person's situation is disregarded, the resulting model is equivalent to following a fixed order of persuasive strategies and, through simulations, we have shown that it can change about 74% of people's situations into one where they are likely to commit to the plan. When the person's situation is included, the percentage rises to 82%, suggesting that the person's situation is also important to consider. Both of these models have advantages and disadvantages, which are discussed and addressed. Thus, this thesis provides two models for a virtual coach that can hold a persuasive dialogue in the context of dyadic planning for physical activity, which can be used as the basis for systems which target behaviour change.

To reduce morbidity and mortality caused by multiple chronic conditions, the number of steps people take each day should be gradually increased. For this, a recommended step goal can be created that is based on an individual's previous walking behaviour. However, for a person, the achievability of this recommended goal can change daily because of that person's state, such as their mood or self-motivation. It could be, for example, that if someone's self-motivation is low, proposing a lower goal than the recommended one, increases their self-motivation and allows them to achieve the recommended goal the next day. Therefore, we investigated the use of a person's state to personalize daily step goal proposals. To do so, we designed and implemented a virtual coach, named Steph, to propose daily step goals to people during an observational study. We used people's states collected in that study to train a reinforcement learning model to optimally personalize the step goal proposals. Based on simulations of our model, we found that people in high states (e.g. who were very motivated and had a positive mood) were more likely to achieve their recommended goals, while people in low states (e.g. who were not motivated and had a negative mood) were less likely to achieve their goals. We also found that proposing higher goals to people in certain states was better than for people in other states. This was because, for some people, a higher goal improved their state while for others, it worsened it. This suggests that personalizing people's step goal proposals optimally could change people's states to where they are more likely to achieve their recommended step goals. So, this thesis provides a model for personalizing daily step goal proposals which can be used as part of behaviour change support systems. It can also serve as a basis for different approaches to predict and change people's walking behaviour to make them more active and less susceptible to chronic diseases.

Seeing the virtual coach as a friend is beneficial as it increases the motivation, confidence, and perseverance of the accompanying. Here, an investigation has taken place to what characteristics the virtual coach must possess to establish this friendly relationship. Thus, the main research question is: What are the reasons for seeing the virtual coach as a stranger or friend? This research made use of pre-gathered data. Here, five hundred participants interacted with the text-based virtual coach Sam in five separate sessions. Afterwards, each participant rated the relationship with the virtual coach, followed by an explanatory free-text response to which thematic analysis was applied. This resulted in five main themes: Relation, Positive Characteristics, Perception, Impersonal, and Chat Opinions. These were used to determine the characteristics the virtual coach must have to be considered a friend. Furthermore, these themes were used to obtain correlations. With these, it has been concluded that the user is more likely to develop a closer relationship with the virtual coach when: perceiving it as a human, pleasantly conversing, and being positive about its character. Whereas the user's age has a negligible influence on the relationship.

The increase in health problems due to smoking or lack of physical activity has created the incentive to develop a virtual coach to help people with smoking cessation and increase physical activity. In this study, a mixed-methods analysis (thematic analysis, quantitative analysis, and a literature study) was carried out to find out what the reasons are for wanting to stop or continue using this virtual coach. By using triangulation, themes were found. The three themes found are the authenticity of the interaction, the caring character of the virtual coach, and the content of the interactions. Based on these themes, recommendations have been given on how to improve the virtual coach. The recommendations suggested are to improve the interactivity of the chat between the virtual coach and the participants, tailor the content of the interactions to the participant’s personal needs, and make use of gamification approaches to increase motivation.

As the awareness of the risks of smoking tobacco increased, usage declined as people started to attempt to quit smoking more. Currently, a wide variety of mobile health (mHealth) applications focused on smoking cessation exist, however users commonly quit these applications over time. In the case of Sam, a conversational agent that plays the role of a virtual coach that helps people to quit smoking and become more physically active, it is still unclear what eases and difficulties users experience when talking to Sam and what others factors might play a role in this. Using a mixed-methods analysis of data, gathered from participants after using Sam, six themes were identified that could provide an insight into these eases and difficulties. The identified themes were conversations feel unnatural, conversations feel natural, clarity towards the user, comfortable to talk, ease of replying and conversations felt impersonal. Recommendations were given for each team on both what to possibly improve and what to leave unchanged. Though users were positive about talking to Sam in general, there still was room for improvement.

As guided interventions are seen as more effective compared to unguided interventions and because the use of eHealth applications is rising over the years, the need to study the effects of a virtual coach as a substitute for guided interventions was created. A virtual coach does not cost as much time and money as a real coach and would thus be beneficial in combination with a virtual smoking cessation application. Therefore, the reasons for preferring to use a questionnaire or virtual coach were studied in this research. Through three questionnaires, 500 participants have indicated their experience with a virtual coach Sam, helping them to prepare to quit smoking. Based on questions about their preference for a virtual coach or a questionnaire, seven themes have been identified. Most of the themes were connected to making the activities proposed by the virtual coach more appealing and therefore the intervention more effective. All in all, the most important aspects of accepting a virtual coach for smoking cessation had to do with the proposed content by the virtual coach.

A virtual coach, Sam, was created by the Perfect Fit project to aid users in establishing a foundation to stop smoking via the means of making them more physically active. Data from 500 users had been collected from them during stages which start with a pre-screening phase until a post-questionnaire phase. In the latter stage, they provided a rating regarding their satisfaction or dissatisfaction with Sam and a free-text response mentioning the reasons why.

Through different means of analysing the data, the research question is answered, which is determining the reasons to be satisfied or dissatisfied with a virtual coach for quitting smoking and becoming more physically active. The three methods of analysis that have been used are a qualitative thematic analysis of the free-text responses, a quantitative analysis of user characteristics and ratings to obtain Pearson correlations, and a literature study.

The findings display five themes which altered users' satisfaction when using Sam. These themes are technical competency, beneficiality, user experience, interrelations and conversationalist. The quantitative analysis had resulted in correlations of at most a moderate degree. However, from the ones which displayed a significant p-value, only 3 correlations against ratings were found and 3 correlations against themes were found. A literature study conducted on outcomes of research from relevant eHealth applications aided the direction of some correlations, such as ones with personality traits. Moreover, their correlation outcomes were compared to shed light on whether the outcomes of this research and previous ones align or differ. Some correlations did align such as with the case of the beneficiality theme against a user's characteristic of conscientiousness and emotional stability.

Lack of physical activity is one of the main risk factors for the development of cardiovascular diseases. Although most people are aware of the risks of this poor health behavior, they have a hard time improving this behavior. Goal-setting is an effective step in the process of changing behavior. It can motivate a person and help them to remain focused on the desired outcome, which increases the chance of successfully achieving a goal. In this thesis, we studied the design of a goal-setting dialogue for a virtual coach to motivate people in the context of physical activity. The dialogue was designed to support people in setting SMART goals and to raise their self-efficacy. Part of the design was vicarious experiences in the form of examples of people who successfully achieved a physical activity goal. We gathered these examples and fit a model to predict which examples to show to a user during the conversation with the virtual coach. An experiment was conducted to evaluate whether there was an increase in users' self-efficacy after the conversation with the virtual coach, how motivating the given examples were, and users' attitudes towards the virtual coach. The results indicated that users' self-efficacy was lower after the interaction with the virtual coach. However, we found that people considered the given examples motivating and had a positive attitude toward the virtual coach.

Smoking is a leading risk factor negatively impacting the health of people, not only those partaking in it first-hand, but also to those around them. Different methods are available to assist people with quitting smoking, with various degrees of effectiveness. Researchers developing smoking cessation approaches would like to have controlled environments to test how effective they are before offering them as viable options. Virtual reality has been demonstrated to be an efficacious tool for facilitating the presentation of cues aimed at eliciting smoking cravings in the lab. Addiction, however, is a complex matter involving different parts of the brain, and how or when conditioned responses causing smoking cravings are activated depend on the individual in question. There are strong indications that personalization of smoking cues, or in other words using elements relevant in the addiction model of an individual, may elicit higher or at least consistent smoking cravings. The general aim is that developing smoking cessation approaches targeting those most relevant elements may have higher ecological validity and therefore be more effective in assisting people both with quitting smoking and maintaining that behavior change. To assist researchers with testing their smoking cessation approaches using a virtual coach, we have created a system enabling the presentation of virtual environments and facilitating communication between researchers and participants while the latter are viewing them. We have evaluated the effectiveness of our system in eliciting a familiar experience, which we posit is a major component tied to smoking cravings. Our results showed that personalized virtual environments elicited a more familiar experience than non-personalized ones. We also examined the usability of our developed user interface, as well as the sense of presence elicited by our system, both of which received positive scores.