Background: Although physical activity (PA) has positive effects on health and well-being, physical inactivity is a worldwide problem. Mobile health interventions have been shown to be effective in promoting PA. Personalizing persuasive strategies improves intervention success and can be conducted using machine learning (ML). For PA, several studies have addressed personalized persuasive strategies without ML, whereas others have included personalization using ML without focusing on persuasive strategies. An overview of studies discussing ML to personalize persuasive strategies in PA-promoting interventions and corresponding categorizations could be helpful for such interventions to be designed in the future but is still missing. Objective: First, we aimed to provide an overview of implemented ML techniques to personalize persuasive strategies in mobile health interventions promoting PA. Moreover, we aimed to present a categorization overview as a starting point for applying ML techniques in this field. Methods: A scoping review was conducted based on the framework by Arksey and O’Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria. Scopus, Web of Science, and PubMed were searched for studies that included ML to personalize persuasive strategies in interventions promoting PA. Papers were screened using the ASReview software. From the included papers, categorized by the research project they belonged to, we extracted data regarding general study information, target group, PA intervention, implemented technology, and study details. On the basis of the analysis of these data, a categorization overview was given. Results: In total, 40 papers belonging to 27 different projects were included. These papers could be categorized in 4 groups based on their dimension of personalization. Then, for each dimension, 1 or 2 persuasive strategy categories were found together with a type of ML. The overview resulted in a categorization consisting of 3 levels: dimension of personalization, persuasive strategy, and type of ML. When personalizing the timing of the messages, most projects implemented reinforcement learning to personalize the timing of reminders and supervised learning (SL) to personalize the timing of feedback, monitoring, and goal-setting messages. Regarding the content of the messages, most projects implemented SL to personalize PA suggestions and feedback or educational messages. For personalizing PA suggestions, SL can be implemented either alone or combined with a recommender system. Finally, reinforcement learning was mostly used to personalize the type of feedback messages. Conclusions: The overview of all implemented persuasive strategies and their corresponding ML methods is insightful for this interdisciplinary field. Moreover, it led to a categorization overview that provides insights into the design and development of personalized persuasive strategies to promote PA. In future papers, the categorization overview might be expanded with additional layers to specify ML methods or additional dimensions of personalization and persuasive strategies.
Games are designed with different objectives in mind. Some primarily for entertainment, others also to educate, motivate or persuade its players. Games with the latter objective, that of persuasion, are designed not only to be entertaining, but also with the intent to shape how players think and feel about issues in reality. However, despite the growing interest in persuasive games, we still lack the design insights and strategies that support their production, particularly for those using immersive technologies. To address this gap, we organize a hands-on workshop and bring together academic and industry experts to explore persuasive game design. Through making we generate knowledge in the form of insights and examplar work, and subsequently formulate best-practises and design strategies for future design and research.
People tend to disclose personal identifiable information (PII) that could be used by cybercriminals against them. Often, persuasion techniques are used by cybercriminals to trick people to disclose PII. This research investigates whether people can be made less susceptible to persuasion by reciprocation (i.e., making people feel obligated to return a favour) and authority, particularly in regard to whether information security knowledge and positive affect moderate the relation between susceptibility to persuasion and disclosing PII. Data are used from a population-based survey experiment that measured the actual disclosure of PII in an experimental setting (N = 2426). The results demonstrate a persuasion–disclosure link, indicating that people disclose more PII when persuaded by reciprocation, but not by authority. Knowledge of information security was also found to relate to disclosure. People disclosed less PII when they possessed more knowledge of information security. Positive affect was not related to the disclosure of PII. And contrary to expectations, no moderating effects were found of information security knowledge nor positive affect on the persuasion–disclosure link. Possible explanations are discussed, as well as limitations and future research directions. Uitgegeven door Sage, APA beschrijving: van der Kleij, R., van ‘t Hoff—De Goede, S., van de Weijer, S., & Leukfeldt, R. (2023). Social engineering and the disclosure of personal identifiable information: Examining the relationship and moderating factors using a population-based survey experiment. Journal of Criminology, 56(2-3), 278-293. https://doi.org/10.1177/26338076231162660
