Abstract
In this paper, we describe the development of a behavior change support system to improve health. The system is designed for people in the age range of 50–65 with an interdisciplinary approach. The basic structure of the presented system consists of two main modules: a monitoring module to collect and analyze data and an intervention module to support behavior changes. Based on the results of a requirements analysis and findings gathered from a conducted literature review and own analyses, the behavior change system addresses the following lifestyle areas: physical activity, nutrition, mental fitness, sleep, and nature contact. We outline how the concept is developed with regards to the results of the requirements analysis and psychological foundations to explain and predict motivation and behavior change processes. We describe how single system components match phases of behavior change models and how they were implemented into an Android application. Finally, we present the results of usability studies where the comprehensibility of the concept and application was tested together with the usability of navigation structure and design. The results show that the target group is able to understand the concept and can navigate through the system easily.
1 Introduction
Technology with the purpose of supporting behavior change touches a variety of research domains and competences that must be included in the design process. We describe a system addressing those implications from different domains. Behavior change models and theories are being studied in psychology and health science. Research in these fields focuses on influencing factors and processes of successful behavior change, and on theoretical models and empirical investigations of their effectivity. Consequently, the competences of those domains are the foundation of a behavior change support system (BCSS) which is defined as
… a socio-technical information system with psychological and behavioral outcomes designed to form, alter or reinforce attitudes, behaviors or an act of complying without using coercion or deception [21].
Such systems make use of persuasive technology—a field integrating human-computer interaction (HCI) and software engineering. In contrast to psychological behavior change models, persuasive strategies [8], [9] employed in persuasive technologies are less complex and more practical and technological, and address single aspects of the before-mentioned theories and models. Examples for persuasive strategies are goal-setting, feedback elements, or tunneling. Furthermore, personalization is a core aspect of persuasive technology, a topic addressed in various areas of software engineering, such as decision support systems, recommender systems, HCI. For advanced personalized and context-adaptive systems, it is more and more common to use sensors to capture context information. This includes environmental information but also the user’s emotional and motivational state, which is valuable for effective persuasion and motivational support. It is subject of electrical engineering to develop sensors and algorithms allowing to derive motivational and emotional user information, thus, this domain plays an increasingly important role in the design of personalized behavior change support systems. However, increasing usage of these technological systems and advanced methods of capturing and interpreting personal user data comes with certain risks. For this reason, it is important to integrate the domain of applied ethics into the process of designing BCSS.
The approach described hereafter is developed in a transdisciplinary way, bringing together competences of relevant and domains. It can be classified as a health-related self-monitoring system as well as a BCSS. It targets users in the age range from 50 to 65 years, which does not mean that the app is in general not suitable for younger or older people, and aims at supporting a healthy lifestyle; integrating different lifestyle areas such as nutrition, physical activity, sleep.
There are already numerous scientific approaches of (mobile) applications which address different health related lifestyle areas or diseases such as physical activity [3], [13], [16], overweight/nutrition [5], [12], [13], or smoking [2], [1], [4], [7]. However, these applications mostly focus on one specific domain. Other applications pursue a more holistic approach to promote a healthier lifestyle or a better well-being (e.g. [18]). These applications often focus on just single persuasive strategies such as monitoring and feedback rather than providing support during the whole behavior change process.
This work describes the conceptual development of a holistic system and its behavior change strategy as well as the evaluation of the user interface of a mobile application implementing the developed concept.
2 Concept Development Process
2.1 Requirements Analysis
In order to investigate the users’ demands and wishes with regards to a health-related behavior change support system, we conducted qualitative interviews with 8 participants (4 male; 4 female) aged between 51 and 58 years (
2.1.1 Attitude Towards Health, Health-Related Behavior Change and Health Goals
Asking participants about their attitude towards health revealed that they have both positive and negative associations distributed equally. Positive aspects mentioned were well-being, vitality, satisfaction, emotions (
Barriers preventing participants from successful behavior changes are lack of motivation (
2.1.2 Persuasive Strategies
Seven participants already had previous experience with monitoring their health, two of them used technical devices and five used pen and paper recording. We asked these seven participants about the achieved effect of monitoring health data. Five of them experienced monitoring as motivating, six stated an improvement of health aspects, and three reported a long-term behavior change. From the participants’ answers it can be derived that the persuasive strategy of (self)monitoring seems helpful, but a more promising solution is to add a support component making of use of persuasive strategies.
To identify, which persuasive strategies should be implemented in the system, we made them subject of interviews. We selected the strategies reduction, tunneling, suggestion, tailoring, personalization, goal-setting, simulation, and social support from the literature [8], [22], and presented them to the participants in the form of short comics. For each strategy, we asked if participants deem them motivating. After having presented all the strategies, we asked participants to indicate their favorites. We excluded participants who indicated that they wouldn’t use a BCSS. Therefore, the sample size was reduced to five.
Participants were very positive towards the strategy of reduction. They liked the idea that a system presents simple rules of thumb instead of complicated instructions. This strategy was seen as crucial by all of the participants. Despite numerous explanations, most participants did not understand the strategy of tunneling correctly, this is why we excluded tunneling in our analysis. Opinions about suggestions made by the system were diverse. Four participants liked such a function, one of them even was euphoric. In contrast, one participant seemed to feel patronized by suggestions. We conclude that suggestions have to be formulated carefully and provide the opportunity to skip or modify it or alternative suggestions to choose from have to be provided. For three out of the five participants the suggestion is a crucial strategy.
Regarding the strategies of tailoring and personalization, the participants had difficulties in differentiating them. As tailoring and personalization are very similar, we analyze the answers together. All participants were very positive about both strategies. They find them for example “very useful,” “very recommendable,” or “a really good help.” Moreover, personalized functions had already been mentioned by some participants in earlier stages of the interview. All five participants indicated that personalization is a crucial strategy. Furthermore, tailoring was seen as crucial by three participants.
Three participants indicated that they find goal-setting motivating. One of them further mentioned the need for support in finding a personally appropriate and motivating goal. Interestingly, one participant who had a negative attitude towards goal-setting, mentioned negative experiences with non-personalized goals, pre-defined by a technical system. These goals were much too high for them and consequently demotivating. Furthermore, the other person rating goal-setting as negative, explains that it feels like a compulsion when a system prescribes a goal that is not set by themselves. These answers reveal that negative attitudes do not refer to the strategy itself. It can be derived that, as discussed above in the context of suggestions, goals should not simply be assigned to the users. Instead, users should be supported in finding an appropriate, personalized goal, but the final choice of the goal should be up to the user.
The strategy of simulation was rated positively by three and negative by two participants. Participants from both camps explained that the content presented using a simulation was not new to them. Simulation was seen as a crucial function only by one participant. Four participants find social support useful, indicating it as a crucial system function.
2.1.3 Data Security and Ethical Aspects
Interviews also revealed that users are concerned with a number of ethical issues. Particularly aspects of data security, privacy, and potential conflict of interest were mentioned. One user claims that it could be potentially harmful if health insurance companies, and employers, or large companies had access to health-related data. Given the sensitivity of health-related data, another user mentioned that storing these data poses a security risk, since data encryption can always be outwitted. So, most users agree that it can never be fully secure to store these data. Another user mentioned that a system of constant surveillance might pose threats to privacy. Depending on who has access to the data, unwanted third-parties such as insurance companies might use the data to impose terms on users. Concerns about reducing a healthy lifestyle to physical health were issued since, as one user avers, psychological factors are much more difficult to track. When asked whether users would be willing to use a system that automatically gathers and stores their personal health-related data, opinions were diverse. Most users agree that their willingness to do so depends largely on what kind of data is monitored, who is granted access to their data, and for what purpose these data are used. If, for example, only their family doctor had access to their data, most users would be willing to automatically collect and store them. With regards to what kind of data users would be willing to share, physiological parameters directly related to their health (such as heart rate, blood pressure, nutrition) were seen as largely harmless data to share. One user mentioned that, in the long run, automatically collecting health-related data in conjunction with institutionally recommended behavior changes might pose a threat to users’ autonomy as such practices might undermine or limit personal choices and freedom, as well as restricting users’ self-reliance. These issues were considered when developing and designing the system.
2.2 Psychological Foundations
There is a wide range of psychological models and approaches trying to explain and predict motivation and behavior change processes. Some of the most influential models of behavior change processes have in common that they distinguish between a motivational phase before and a volitional phase after building an intention for behavior change. Within the Rubicon Model [14], [15] the change of those two main phases is called “crossing the Rubicon River” (using the metaphor of Caesar crossing the Rubicon River, what revealed his intentions as it was a point of no return). Further milestones are the initiation and the realization of the intentions. In between there are the pre-decisional, pre-actional, actional, and post-actional stages (see Figure 1). Each of these stages of the behavior change process can and should be supported.
![Figure 1
Rubicon model of action phases following Heckhausen and Gollwitzer [14] and Heckhausen and Heckhausen [15].](/document/doi/10.1515/icom-2017-0008/asset/graphic/j_icom-2017-0008_fig_022.jpg)
![Figure 2
Health Action Process Approach adapted from Schwarzer [27].](/document/doi/10.1515/icom-2017-0008/asset/graphic/j_icom-2017-0008_fig_023.jpg)
Health Action Process Approach adapted from Schwarzer [27].
Similarly, the Transtheoretical Model [24], [25] distinguishes between five stages:
Pre-contemplation: The person does not yet have the intention of behavior change within the next six months.
Contemplation: The person has built the intention of behavior change within the next six months.
Preparation: The person has the intention of behavior change within the next 30 days and has already made first steps.
Action: The person has changed their behavior for less than six months.
Maintenance: The person has changed their behavior for more than six months.
Furthermore, following the Health Action Process Approach [26], [27], [28], the behavior change process consists of several stages, which are intention (motivational phase), planning (volitional phase), initiative, maintenance, and recovery. They are influenced by self-efficacy, outcome expectancies, and risk perception as illustrated in Figure 2.
The concepts have the assumption in common that intentions building is a necessary but not sufficient condition for successful behavior change. Moreover, following the models, it is possible and useful to offer support along the whole process. In all concepts, goal-setting plays an important role as goals are measurable intentions. Moreover, action planning, as one step of the volitional phase is another important aspect. We developed a system concept, described in the following section, that provides user support along the whole process of behavior change.
2.3 Health Science Foundations
In addition to the conducted interviews from the requirements analysis, several steps were taken to complement the identified health-relevant lifestyle areas. Based on a health science literature review, occurrence and weighting of 48 individual lifestyle areas used for calculating lifestyle scores were analyzed. Furthermore, in a separate analysis of a large cohort study (
Behavior Change Strategy of the proposed system.
Lifestyle-related problems first manifest themselves in old age [17]. We therefore address target users in the age 50+, since the own health status in everyday life becomes more important for older age groups compared with younger age groups [29]. Thus, the motivation to do something for health and well-being is more likely. Through preventive and health-promoting measures (primary prevention), the age of onset of chronic illness may be postponed [10]. Therefore, the BCSS should aim at health promotion and prevention. Another reason for targeting best agers instead of elderly (65+) is that a common reason why a healthy behavior such as healthy eating cannot be executed is lack of time [6], [20]. Lack of time mainly affects employed persons. That is why we have focused on people over 50 years who have not reached the retirement age yet. Furthermore the user’s context information should be considered by the system to make behavioral changes more likely and give personalized recommendations.
2.4 System Concept and Behavior Change Strategy
Based on the requirement analysis and literature foundations, we developed a behavior change strategy and system concept that integrates appropriate measures to support users during the whole behavior change process.
Taken together, the results from the conducted interviews and health science foundation, we chose five lifestyle areas to be addressed for behavior change support. Those lifestyle areas are physical activity, nutrition, mental fitness, sleep, and contact with nature which includes aspects of daylight consumption in natural environments or exposure to fresh air.
Mapping of system components of the behavior change strategy to behavior change models.
| System Component | Health Action Process Approach | Transtheoretical Model | Rubicon Model | ||
| Monitoring Module | Information / Monitoring | Motivation | Pre-contemplation | Motivation | Pre-decisional phase |
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| Intervention Module | Overall Goal | Motivation | Pre-contemplation, Contemplation | Pre-decisional phase | |
| Selection of Lifestyle Areas | Motivation | Contemplation | Pre-decisional phase | ||
| Goal-Setting | Motivation | Contemplation, Preparation | Pre-decisional phase, Pre-actional phase | ||
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| Action Planning | Volition | Preparation | Volition | Pre-actional phase | |
| Realization Support | Volition | Action | Actional phase | ||
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| Feedback | Volition | Action, Maintenance | Motivation | Actional phase, Post-actional phase | |
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| Monitoring Module | Monitoring | Volition | Maintenance | Post-actional phase | |
The support process refers to the motivation phase as well as to the volitional phase described in various psychological behavior change models. Table 1 shows the mapping of our system components to the underlying psychological behavior change models, namely the Health Action Process Approach, the Transtheoretical Model and the Rubicon Model. In what follows, we describe the behavior change strategy and the corresponding system components.
The system we developed consists of a monitoring and an intervention module (see Figure 3). The monitoring module is used to track a wide range of health-related lifestyle data (via sensors and/or manual user-input). Monitoring is not part of the core intervention process which starts with the user’s decision to change their behavior. However, the monitoring module is part of the behavior change strategy; namely the pre-contemplation phase referring to the Transactional Model. From a persuasive point of view, monitoring aims at three major objectives:
Demonstrating problematic areas and the relevance of behavior change
Fostering awareness for maladaptive behavior
Increasing self-efficacy by demonstrating that the user can influence their health
Another aspect of the pre-contemplation phase is demonstrating the relevance of specific behaviors. Information should boost the persuasive effect of monitoring: the monitoring module aims at convincing the user to build an intention for behavior change.
Subsequently, the intervention process starts when the user already has an intention to change their behavior. The system’s intervention module is designed as a guided process, implementing the persuasive strategy of tunneling to support users by showing them only the necessary following step.
2.4.1 Overall Goals
The core intervention process starts with the user’s intention to change their behavior. This is related to a, mostly unspecific, goal which is, at this stage, usually not yet connected to specific behavior. As previously mentioned, our requirements analysis revealed that potential users do not define their goals as measurable behavioral goals (e.g. “I want to make 10 000 steps a day.”). They often do not even refer to a specific lifestyle area (e.g. “I want to increase my physical activity.”). Instead, they formulate overall goals referring to a future state such as improving metabolism. Taking a desired state as a starting point of the behavior change process may also support transition from the pre-contemplation to contemplation phase referred to in the Transtheoretical Model. Thus, the guided behavior change process in our system starts with a selection of one or more overall goals.
2.4.2 Lifestyle Selection
In the next step, the selected overall goals are mapped onto the lifestyle areas that influence the achievement of overall goals. The lifestyle areas in which behavior changes are recommended are presented in a ranked order based on the level of influence on the overall goal and the user’s current state in a specific area; accordingly, areas that need most improvement are prioritized. The user chooses one lifestyle area to start an intervention. The system does not allow for selecting more than one area at the same time so that the user can concentrate on one goal.
2.4.3 Goal-Setting
The persuasive strategy of goal-setting is well investigated. Following goal-setting theory, goals have to be attainable, challenging, and measurable. Referring to our requirements analysis, some participants found the strategy of goal-setting motivating. However,the objections of those participants who had a negative attitude towards goal-setting show that it is not goal-setting itself they dislike. Rather, they criticized approaches where a fixed, non-personalized goal is assigned to the user. Taking these objections and demands seriously, our system includes goal-setting that supports users in finding a motivating goal level. After having selected a lifestyle area, a concrete behavioral goal is suggested to the user. The suggested goal level is personalized in a way that it is challenging but not overburdening for the specific user. The algorithm that was used to calculate the goal is described in earlier work [16]. The user has the opportunity to select the recommended goal or modify it to make it adaptable to specific contexts and situations the system cannot be aware of, and to prevent paternalism. This approach is also supported by goal-setting theory, as self-set goals have found to be more effective than assigned ones.
Calculating a goal recommendation is not always applicable. For example, the lifestyle area of nutrition is a very complex field where exact recommendations have two major disadvantages: Firstly, well-founded, reliable nutrition recommendations based on a wide range of medical information about the user, e.g. specific blood values, that would have to be checked periodically. However, currently, self-monitoring systems are not able to measure all of the needed parameters. Secondly, an exact recommendation would be too complex for most of the users as a wide range of nutrients would have to be considered. Complex goals are hard to achieve and thus less motivating. This is why, in this case, we use the persuasive strategy of reduction, which was rated high in our requirements analyses. Goal suggestions base on rules of thumb (e.g. “Eat five portions of fruit and vegetables every day. Consider having a colorful mix.”) that were proven to be reasonable by health science research. The choice between different suggestion or the adaptation of the goal level is up to the user.
The step of goal-setting marks the transition from contemplation to the preparation phase, referring to the Transtheoretical Model, respectively from the pre-decisional to the pre-actional phase, referring to the Rubicon Model. The user metaphorically crosses the Rubicon River. It does also indicate the end of the motivational phase and the beginning of the volitional phase.
2.4.4 Action Planning
The volitional phase starts with action planning, which is called the pre-actional phase in the Rubicon Model, respectively preparation in the Transtheoretical Model. A technique for action planning suggested by Gollwitzer [23] are so-called implementation intentions. This technique serves to achieve a specific goal, by building action plans or so-called if-then plans. To create these plans an anticipated critical situation will be associated with a goal-directed behavior. This plan will be formulated as If situation x arises, then I will initiate the goal-directed response y [23]. According to Gollwitzer & Sheeran [11] the if-then plans have to be precise, viable and instrumental. The if-component can relate to situational cues (e.g. time, location, persons) or inner cues (e.g. feelings, thoughts). The then-component can either initiate or maintain a desired behavior or suppress an unwanted behavior [11].
2.4.5 Realization Support
Based on the defined measurable goal and the action planning, the user initiates their action in the next step. The system includes different features to support the user during the actional phase. Examples are user and context adaptive walking routes or offering smart reminder functions (for example considering goal progress, calendar entries, and meteorological data). It is up to the user’s choice which support features they activate.
2.4.6 Feedback
Continuous feedback on progress towards the goal is provided to support users in the actional and post-actional or maintenance phase. It is helpful to evaluate and, if necessary, to modify one’s efforts. If users have already established new habits, relevance of goals and feedback on goal progress decreases. Instead, it is important to maintain the new behavior. This process can last up to a few years. During the maintenance phase, the system provides support by the monitoring function. It is not goal-bound, but enables the user to continuously evaluate their lifestyle so that they recognize relapse in an early stage and can intervene.
3 Application and Interface Development
After developing the concept of the system, the next step was to create an application that implements all its aspects. We decided to develop a mobile application for android smartphones. This application includes the monitoring and the intervention module as main components. Additionally, we added a news and information module; for example, to inform users about new questionnaires and updates. To consider concerns about data security, we decided to outsource data collection (e.g. tracking GPS signal or physical activity) in separately installable plug-ins. This enables users to activate/deactivate each function separately, and to control data tracked by the system.
The app’s home screen represents news module, intervention module, and monitoring module as cards.
Due to the concept’s complex structure and large amount of content elements, one of the major challenges was to design a clean and comprehensible interface and navigation structure tailored specifically to the target group. During the development process, different stages of the prototype were evaluated, and the system was changed iteratively according to the feedback obtained.
In an initial formative evaluation, using the think-aloud protocol, we examined preferences for basic interaction elements and navigation structures in short interviews. In these interviews, existing health monitoring applications were presented to eight participants (4 female, 4 male) in the age range between 55 and 67 (
Layout of the intervention module. The screen shows the overall goals, the specific goal, the action plans, and the realization support represented as cards.
The clickable dummy was evaluated in a second formative evaluation with eight participants (4 female, 4 male) in the age group of 50 to 65 (
Users rated the tutorial as sufficient and comprehensible; however, results reveal that the participants did not read the tutorial with full attention. One reason could have been that parts were missed because participants didn’t recognize that screens were scrollable. The evaluation of the main page showed that the news and information card was perceived less than the other cards due to missing icons, but the page was rated as understandable and not convoluted. The goal-setting process was rated as logical and not too long by all participants. Foreign words and technical terms, even if they are explained in the tutorial (e.g. specific measurement units) led to a lack of understanding. Three participants stated that they would use route suggestions anywhere, whereas four participants stated that they would use it only in unknown areas (e.g. during holidays). Participants had no problems operating the monitoring module. When asked, all participants deemed the navigation structure of the monitoring module adequately designed. Likewise, the participants were able to navigate to the statistics view over the details/statistics icon.
In summary, it can be stated that the app and especially the intervention module has a clear navigation structure and is comprehensible. The tutorial screens should be changed; scrollable screens should be spread over multiple screens to avoid scrolling. To avoid a lack of understanding, ordinary language and additional explanations should be used in all parts of the app. Based on these results, the final navigation structure and design guidelines were developed. Accordingly, we developed a first version of the Android application including the whole navigation structure and some monitoring components. This app was evaluated in a third study.
Procedure of the summative evaluation.
Aim of the third study was a summative evaluation of the concept, navigation structure and design on a smartphone with focus on our target group. For this reason, we conducted a study with 20 participants (11 female, 9 male) in an age range between 50 and 68 (
The participants rated the tutorial as “rather helpful” (
The goal-setting process was overall rated positively (temporal extent:
The participants were able to operate the monitoring component of the app without any problems and rated this task as (mostly) easy (
Overall, the scores of the System Usability Scale (
Screenshots of the app’s home screen in different color schemas.
4 Conclusion and Outlook
In this work, we described the conceptualization and development of a behavior change support system to promote a healthy lifestyle in aging. Following a transdisciplinary research approach, various scientific domains as well as the users were included in the design process. To gather knowledge about the target group’s needs, we conducted a requirement analysis. In this analysis especially the attitude towards health, health-related behavior change, health goals, preferences for persuasive strategies, data security, and ethical aspects were addressed.
Another foundation for the system concept were domain-specific research findings gathered from a conducted literature review and own analyses. From user input and expert knowledge, we first defined the lifestyle areas the system will address, which are physical activity, nutrition, mental fitness, sleep, and nature contact. Moreover, based on psychological foundations and the target group’s preferences in persuasive strategies, we conceptualized an integrated, comprehensive behavior change support system. The system mainly consists of a monitoring component and an intervention module. It offers step-by-step guidance through the whole behavior change process, starting with so-called overall goals, supporting in finding a precise, motivating and expedient behavior goal, support in action planning, offering realization support and adequate feedback. This support process is characterized by highly personalized assistance.
Furthermore, we described how this theoretical approach was transposed into a smartphone app designed for our target group. Due to the large amount of content elements and functions, one of the major challenges was to keep the interface clear and avoid unnecessary complexity when mapping the conceptual elements onto the interface.
Preliminary evaluation results are positive regarding navigation structure, layout and design, and the behavior change strategy. The described system implementation and (interface) evaluations do not yet include advanced personalized functions as intended in the concept. Consequently, our next steps in developing the system are implementing intelligent, adaptive algorithms. This includes, among other things, adaptive goal recommendations considering the user’s fitness level, context, and volition or adaptive recommendations of the implementation intentions.
About the authors
Katja Herrmanny, M. Sc. works as a researcher in the Personal Analytics research group of the Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen. Her research focuses on user- and context-adaptive recommendations in the field of health‐related persuasive and motivational applications as well as interactive systems for elderly users. She holds a Master of Science in Applied Cognitive and Media Science (with honor) with computer science being her major subject.
Michael Schwarz, B.Sc. works as a researcher in the Personal Analytics research group of the Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen. His research focuses on user modeling and context based recommendations. He holds a Bachelor of Science in Applied Cognitive and Media Science.
Dr. Katrin Paldán works as a researcher in the Personal Analytics research group of the Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen. As a health scientist she is a member of the Center of Urban Epidemiology at the Essen University Hospital. Her research focuses behavioral and environmental-based prevention and health promotion, taking account of digital innovations. She received her doctorate at the chair of Sport and Health Sciences at the University of Stuttgart.
Nils Beckmann, M. Sc. works as a researcher in the Personal Analytics research group of the Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen. His research focuses on the development of wearable devices for affective computing applications. He holds a Master of Science in Electrical Engineering.
Jennifer Sell, B. Sc. worked as a student assistant in the Personal Analytics research group of the Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen. She holds a Bachelor of Science in Applied Cognitive and Media Science and is currently studying the master program of Applied Cognitive and Media Science.
Dr. Nils-Frederic Wagner works as a researcher in the Personal Analytics research group of the Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen. His research focuses on the ethics of persuasive technologies and broader issues in normative ethics and philosophy of mind and action. He holds a PhD in Philosophy.
Dr. Aysegül Dogangün works as a researcher and group manager in the Personal Analytics research group of the Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen. She holds a PhD in Computer Science.
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