This paper presents the results of an experimental field study, in which the effects were studied of personalized travel feedback on car owners’ car habits, awareness of the environmental impact of their travel choices, and the intention to switch modes. For a period of six weeks, 349 car owners living in Amsterdam used a smart mobility app that automatically registered all their travel movements. Participants in the experiment group received information about travel distance, time, and CO2 emission. Results show that the feedback did not influence self-reported car habits, intention, and awareness, suggesting that personalized feedback may not be a one-size-fits-all solution to change travel habits.
Obesity has become a major societal problem worldwide [1][2]. The main reason for severe overweight is excessive intake of energy, in relation to the individual needs of a human body. Obesity is associated with poor eating habits and/or a sedentary lifestyle. A significant part of the obese population (40%) belongs to a vulnerable target group of emotional eaters, who overeat due to negative emotions [3]. There is a need for self-management support and personalized coaching to enhance emotional eaters in recognising and self-regulating their emotions.Over the last years, coaching systems have been developed for behavior change support, healthy lifestyle, and physical activity support [4]-[9]. Existing virtual coach applications lack systematic evaluation of coaching strategies and usually function as (tele-)monitoring systems. They are limited to giving general feedback to the user on achieved goals and/or accomplished (online) assignments.Dialectical Behavior Therapy (DBT) focuses on getting more control over one’s ownemotions by reinforcing skills in mindfulness, emotion regulation, and stress tolerance [10]. Emotion regulation is about recognizing and acknowledging emotions and accepting the fact that they come and go. The behavior change strategies within DBT are based on validation and dialectics [11]. Dialectics changes the users’ attitude and behavior by creating incongruence between an attitude and behavior since stimuli or the given information contradict with each other.The ultimate goal of the virtual coach is to raise awareness of emotional eaters on their own emotions, and to enhance a positive change of attitude towards accepting the negative emotions they experience. This should result in a decrease of overeating and giving in to binges. We believe that the integration of the dialectical behavior change strategies and persuasive features from the Persuasive System Design Model by Kukkonen and Harjumaa [12] will enhance the personalization of the virtual coach for this vulnerable group. We aim at developing a personalized virtual coach ‘Denk je zèlf!’ (Dutch for ‘Develop a wise mind and counsel yourself’) providing support for self-regulation of emotions for young obese emotional eaters. This poster presents an eCoaching model and a research study protocol aiming at the validation of persuasive coaching strategies based on behavior change techniques using dialectical strategies. Based on the context (e.g., location), emotional state of the user, and natural language processing, the virtual coach application enables tailoring of the real-time feedback to the individual user. Virtual coach application communicates with the user over a chat timeline and provides personal feedback.The research protocol decribes the two weeks field study on validating persuasive coaching strategies for emotional eaters. Participants (N=30), recruited via a Dutch franchise organization of dietitian nutritionists, specialized in treating emotional eating behaviors, will voluntarily participate in this research study. Participants will be presented with short dialogues (existing questions and answers) and will be asked to select the preferred coaching strategy (validating or a dialectical), according to their (current) emotions. To trigger a certain emotion (e.g., the affect that fits best with the chosen coaching strategy), a set of pictures will be shown to the user that evoke respectively sadness, anger, fear, and disgust [13].Participants will be asked to fill out the demographics data ((nick) name, age, gender, weight, length, place of residence) and three questionnaires: • Dutch Eating Behavior Questionnaire (DEBQ) [14],• Five Factor Personality Inventory (FFPI) [15], • Quality of Life Index Questionnaire [16].This research study aims at answering the following research questions: “Which coaching strategies do users with a specific type of emotional eating behavior benefit most from while consulting their personalized virtual coach?; “Which coaching strategies are optimal for which emotions?” and “Which coaching approach do users prefer in which context, e.g. time of the day, before/after a craving?”
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Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
