ABSTRACT The opioid overdose epidemic constitutes a critical public health crisis, necessitating advanced surveillance tools to enable timely intervention. Social media platforms provide a real‐time source of information on drug‐related behaviours. However, extracting structured knowledge from their informal, slang‐heavy and fragmented text presents significant technical challenges. While Named Entity Recognition (NER) enables the automated identification of drug‐related entities. Prior work has focused mainly on general biomedical domains, with limited exploration of domain‐specific scenarios. To address this gap, this study makes three key contributions. First, we present the first manually annotated, domain‐specific NER dataset for opioid overdose, comprising eight unique entity types, with a particular focus on routes of administration, sourced from Reddit posts spanning January 2021 to December 2023. Second, we provide a detailed description of the annotation process and guidelines, and systematically discuss challenges encountered during annotation, including slang, fragmented expressions and ambiguous language commonly found in social media posts. Third, we conduct an extensive series of experiments, including classical machine learning, deep learning with pretrained embeddings combined with CRF decoding, and transformer‐based models, evaluated at both token and entity levels. The proposed model achieved strong and balanced performance, with F1‐scores of 0.891 at both token and entity levels, highlighting the effectiveness of domain‐specific modelling for opioid overdose surveillance on social media.

Sensor-based technologies for healthy eating have gained attention in human-computer interaction research. With the prevalence of diet-related health issues such as obesity and diabetes, innovative interventions providing personalized dietary feedback are increasingly needed. Sensor-based technologies offer real-time monitoring of dietary habits. We reviewed 28 articles, published between 2014 and 2024, to uncover the state-of-the-art and research gaps. Sixteen articles (57.1%) reported on effectiveness: seven measured by machine learning model performance and nine through user study evaluation. We identified several eating behaviors that were supported: adequate food intake, meal size and quality, eating posture, swallowing sounds and difficulties, eating styles, and pace. These behaviors were associated with conditions such as diabetes, post-stroke issues, cardiovascular diseases, obesity, and binge eating. Limitations included little to no interventions for life-threatening conditions. Our findings present opportunities to develop tailored sensor-based interventions for various diet-related issues.

The global prevalence and popularity of video gaming continue to rise, with games rapidly advancing in graphics, controls, storylines, and device hardware optimization. Today, video games span a wide range of platforms, including desktops, smartphones, tablets, consoles, virtual reality (VR) headsets, and even smartwatches. Despite platform differences, all games are designed to engage players and foster a positive player experience (PX), driven by diverse motivations. In this digital age, players frequently share feedback through star ratings and descriptive reviews, offering valuable insights into playability and PX. Analyzing this user-generated content is essential for understanding current trends and improving player-centric game design across platforms. This study focuses on smartphone games, motivated by the widespread global use of smartphones. We analyzed 4,595 player reviews from 289 smartphone games across 33 genres on Android and iOS platforms. Our three-step process involved: (1) collecting player reviews, (2) conducting binary sentiment analysis using both machine learning (ML) and lexicon-based approaches, and (3) performing thematic analysis on reviews classified by the best-performing sentiment analysis method. We found that the lexicon-based approach outperformed the machine learning approach, achieving an F1 score of 0.91 compared to the best performing ML model’s F1 score of 0.85. While the thematic analysis approach identified 23 playability and player experience factors grouped into four main categories: in-game advertisements (6 factors), sales and customer service (5 factors), technical issues (9 factors), and game design and gameplay (3 factors). Within the game design and gameplay category, we further identified a total of 34 sub-factors distributed across its three factors i.e., game design (3 sub-factors), gameplay (23 sub-factors), and players’ subjective perspectives (8 sub-factors). This study contributes to the fields of games user research, smartphone games, and player experience through analysis of reasonably large number of player reviews from a significant sample of smartphone games. Further, this work lays the groundwork for more specialized and in-depth research in smartphone gaming domain, thereby deepening our understanding of playability and player experience of smartphone games as well as advancing the notion for a platform-centric game design.

Suicide is a complex phenomenon wherein, in addition to the individual impacted, its effects seep into many lives including those of their caregivers. Caregivers seek help everywhere but face unique challenges including limited access to timely resources and personal mental health struggles. Mobile health apps offer a promising solution, but addressing caregivers’ specific needs remains a concern. We present LifeLink, a persuasive mobile app to support caregivers of individuals experiencing suicidal thoughts. The app was developed in three stages. First, we reviewed 80 existing suicide prevention apps. Second, we designed a low-fidelity prototype of LifeLink using the Persuasive System Design model and refined it through a study conducted with 45 caregivers. Finally, incorporating evidence-based strategies and caregiver feedback, we developed and evaluated LifeLink in another study with 50 caregivers. Results show that LifeLink is user-friendly, engaging, elicits positive user experience and effectively empowers caregivers. LifeLink usage was associated with improved mental wellbeing, increased mental health literacy, and a more supportive environment. Our findings highlight the importance of involving caregivers in the design process. We offer recommendations for designers and researchers developing impactful persuasive technology for suicide prevention and for those working in related areas.

In the evolving landscape of social media, distinguishing between bots (automated accounts) and real human users remains a significant challenge. This research aims to address this challenge by focusing on the behavioral patterns of users on X (formerly Twitter). We propose an early-warning system based on representing user tweeting behavior as binary sequences. Thus, the approach does not require content analysis, enabling a lightweight yet effective solution. We evaluated the proposed approach on a wide spectrum of datasets, with promising results. This method offers new insights that could help to improve bot-detection tools on social media platforms such as X.

Social media users normally congratulate each other with goodwill messages on their birthdays. This can unintentionally reveal personal information and possibly compromise privacy, leading to potential risks. This research examines birthday disclosure behaviors on social media across northern and southern Nigeria. Through a detailed analysis of 700 participants, the study investigates the impact of regional distinctions on social media privacy practices. Despite initial hypotheses of significant regional differences, our analysis, which employed descriptive and structural equation model (SEM) methodologies, revealed a surprising similarity in perceptions and behaviors toward social media disclosures across both regions. Social gratification was a major influence on disclosing birthday information, irrespective of the region. The thematic analysis suggests that Northerners would consider if disclosing their birthday information would increase their reputation, while Southerners consider receiving gifts, wishes, and celebrations on their birthdays. Unlike in the South, some sections of the North also noted religious reasons as a concern for not disclosing their birthday. This study contributes to the global dialogue on digital privacy but also suggests a shared digital culture within Nigeria, providing insights for developing culturally sensitive privacy policies and practices.

To enhance the persuasiveness of behaviour-change games, designers employ persuasive strategies. These persuasive strategies are intended to motivate the users towards the desired behaviours. Hence, the motivational appeal of these persuasive strategies can play an important role in the effectiveness of these behaviour-change games. Furthermore, research has shown that game framing can impact its effectiveness. Therefore, it is important to understand how the type of framing employed in the game impacts the effectiveness of persuasive strategies and their motivational appeal. To advance research in this direction, this paper explores the relationship between the perceived effectiveness of four popular persuasive strategies (reward, competition, praise and suggestion) and their motivational appeal in a persuasive game for healthy eating across the three different game framings: gain-framing, loss-framing or gain-loss-framing. In a study of 371 participants, our results revealed that all the persuasive strategies were perceived to be significantly effective across all game-framing versions. We also discovered that game framing had varying significant impacts on the relationship between the perceived effectiveness of persuasive strategies and their motivational appeal. We conclude by offering some insights on how to implement persuasive strategies to design games with better persuasive motivational appeal.

Problematic smartphone use (PSU) is a growing concern that hurts users' daily lives and activities. Previous studies have emphasised the importance of developing mindfulness and self-efficacy concerning smartphone use rather than solely focussing on reducing usage. However, there has been little research developing and evaluating digital interventions specifically targeting absent-minded smartphone use. There is also little knowledge on how best mindfulness practice using digital technologies can be integrated with persuasive designs which have been widely studied and implemented for behaviour change. In this paper, we developed a live wallpaper application for Android lock and home screens as a mindfulness-based intervention for absent-minded smartphone use. The application was evaluated over two weeks with 121 participants. This was followed by a semi-structured interview with 15 participants. The results of our analysis show that the intervention reduced absent-minded smartphone use overall. While participants found the various features (especially the customisation features) of the intervention to be persuasive, we found no correlation between perceived persuasiveness and behaviour change. This work provides valuable insights for advancing human-computer interaction (HCI) research on PSU. Additionally, the results raise important questions for future research, such as the relationship between perceived persuasiveness and behaviour change. Finally, we contribute to the discussion on the applications of mindfulness in persuasive technology, challenges and future research areas.

The emergence of large language models (LLMs) is transforming various health-related domains, including approaches to obesity management. Obesity remains one of the world’s leading health issues, prompting the research community to develop various weight-loss applications focused on physical activity, dietary planning, and related interventions. In this study, we explore the capability of the LLM ChatGPT for personalized dietary planning. We conducted two case studies: Case Study 1 examined self-supervised recipe generation using ChatGPT alone, while Case Study 2 investigated a self-supervised approach combining National Institute of Health standards with ChatGPT recipe recommendations. We also performed a user study to evaluate recipe recommendations from ChatGPT. Our results show that ChatGPT recommends appropriate recipes based on comparisons with the United States Department of Agriculture’s (USDA) recipe calculator. We found no significant difference between ChatGPT-generated recipe recommendation calories and USDA standards for either Case Study 1 (p = 0.8530) or Case Study 2 (p = 0.0687). In addition, we found significant weight loss in participants following these recipes in both Case Study 1 (p < 0.00001) and Case Study 2 (p = 0.0014). Furthermore, the user study with potential weight-loss participants revealed varying levels of satisfaction (p = 0.001) and identified themes related to meal preferences, effective prompt generation, and mixed concerns regarding privacy, trust, user consent, and data storage. We conclude by discussing additional findings from our case and user studies, and present opportunities, challenges, and design and ethical considerations for the research community.

In recent years, there has been a notable rise in the development of mobile apps to deliver stress management interventions. However, the prevailing design of most available stress management apps leans towards an emotion-focused coping approach, primarily targeting the regulation of stress-induced negative emotions. Given that the perception of time shortage is a major source of stress among students in higher education, we adopted a problem-focused coping approach that targets tackling student stress via time management. Our work evolved through four main phases. First, we previously conducted a large-scale study involving 502 students, constructing five structural equation models (SEMs) to pinpoint the most effective time management factor in promoting the perception of control over time. Second, based on the findings, we designed and developed a persuasive mobile app (SortOut) to promote effective time management behavior among the target users. Before the development phase, the initial app prototype was evaluated and refined with students (n = 69). In the final phase, this paper focuses on the culmination of our work, wherein we assessed the app’s effectiveness through a 4-week field study involving 34 students. Subsequently, we conducted one-on-one interviews with 11 students to delve into their experiences and feedback. The results revealed that, after using the app, students demonstrated improvement in time management behavior, higher academic self-confidence, and lower stress compared to the baseline. Students in the early and later stages of behavior change (based on the Transtheoretical Model (TTM)) reported similar positive outcomes. Moreover, students perceived the app as straightforward and easy to use; they were not tense or pressured while using the app, which is especially vital for stress management interventions. Thematic analysis showed that the app encouraged organizational thought and behavior and aided students in managing their time and workload, increasing the commitment toward task completion. The study findings suggest that the app helped students engage in effective time management behavior with an improved perception of control over time. Such improved perception is instrumental in promoting student confidence and well-being. Guided by the study findings, we provided actionable design recommendations and future research directions to facilitate the development of impactful persuasive time and stress management interventions.

Student stress poses a widespread challenge, significantly impacting academic performance and mental well-being. Regrettably, stigma and accessibility barriers often discourage students from seeking help. Nonetheless, a ray of hope emerges through persuasive digital interventions (PDIs). These interventions, with their potential to foster positive behaviours in health and wellness, offer a promising avenue to address the complexities of student stress. Understanding how PDIs motivate behavioural change is pivotal for developing effective stress management solutions. This systematic review analyzes papers spanning two decades on PDIs for managing student stress, with the goal of synthesizing methodologies and approaches for designing, developing, and evaluating these interventions. We explore trends, considering factors such as evidence-based therapy, type of stress interventions, digital platforms for delivery, frequently employed persuasive and behaviour change strategies, and evaluation methodology. Additionally, we examine the effectiveness of PDIs in reducing student stress and examine the relationships between effectiveness and design strategies. Finally, our study contributes to the fields of human-computer interaction and mental health by identifying shortcomings and gaps in the existing literature. We propose directions and potential research questions to guide future initiatives in these fields.

Persuasive games, designed and tailored to change users’ behavior, have enhanced user experience across various domains. However, their role in emotion regulation remains understudied, despite the increasingly immersive environments they create that trigger emotional responses. Sensors have emerged as key artificial intelligence (AI) tools in persuasive games supporting emotion regulation compared to other AI technologies, such as chatbots. Yet the effectiveness, challenges, and applications of these sensors lack comprehensive exploration. Our scoping review analyzed 32 articles published from 2013 to 2023 on persuasive games using AI-based sensing technologies for emotion regulation. Results from 27 articles (84.8%) reporting the effectiveness of sensors revealed mostly large to moderate effects (d ≥ 0.8 ≤ 0.4). Common challenges with integrated sensors (camera systems, facial recognition, smartwatches, and altimetric pressure sensors) included emotion detection accuracy and ethical concerns. We provide design recommendations for developing ethically sound and effective persuasive games for emotion regulation in the future.

In recent years, there has been growing research interest in aligning technological designs with users’ lived experiences. The goal of this work is to review existing work on the current state-of-the-art on incorporating individual and community values and beliefs into various kinds of interactive computerized technologies, emphasizing socio-cultural sensitivity. After screening 235 records, 45 papers were included in this review. Our research reveals that researchers are at the forefront of developing advanced socio-cultural digital tools and interactive educational platforms. They frequently employ techniques like collaborative dialogue facilitation, personalized linguistic support, and the integration of culturally significant design principles, such as cultural narratives and symbols. This commitment to technology’s transformative potential extends beyond education, making its mark in healthcare, social networks, finance, and other domains. We conclude by providing an overview of the questions that other researchers can investigate in the future for designing technologies that are socio-culturally sensitive. Future studies would benefit from a wider use of theories to account for the complexity of human behavior while designing socio-culturally sensitive technologies.

Exergames are becoming increasingly popular and have shown potential for motivating physical activity. Past research suggests that social (multiplayer) exergames offer players an engaging experience and good aerobic exercises. Our systematic review summarizes existing work and identifies gaps, trends, and patterns on social exergame research in the domain of health and wellness. A search was conducted in the ACM Digital Library, IEEE Xplore, and PubMed. After screening 2272 records, we identified 73 studies from 2013 to 2023 that meet the inclusion criteria. Our results reveal that step tracking is the most commonly implemented measure of physical activity in social exergames, and that competition, rewards, and cooperation are the most common features used for designing the games. Our results also show that the effectiveness of social exergames is intricately linked to a combination of factors, including group size, player matching, and game features. The main contribution of this paper is (1) an analysis of features and group dynamics employed for designing social exergames, and (2) how game features affect the games’ outcome (both positive and negative) uncovering challenges and opportunities to advance future research in this area. Our findings in the current review provides insights for the design and implementation of social exergaming helping users to experience more socially satisfying game experiences thereby increasing the motivation for exercise, as well as gaining social benefits.

mHealth apps and tracking technology are becoming popular because they help people adopt a healthier lifestyle and form healthy habits. One way mHealth apps can help users is by presenting visuals to help them better understand their health data. Commendable efforts have been carried out to personalize health-promoting interventions to help users become more aware of their own health, and health practitioners for providing healthcare services. For example, digital self-tracking apps nowadays can be used to monitor physical activity, nutrition and sleep patterns. This systematic review aims to investigate the current trends, challenges, gaps, and opportunities in health visualizations on mobile devices. Peer-reviewed papers in English collected using online databases (ACM Digital Library, PubMed, and Web of Science) from 2012 to 2022 were considered, and 56 studies were selected out of 1,168 studies. Results showed that among 11 different health domains, general health and physical health were the most heavily studied. Relatedly, results also showed that physical fitness data is the most frequently collected data type automatically from sensors/trackers. Furthermore, bar and line charts are the most popular type of visualizations used for presenting a variety of health data, and while most apps present static visualizations, interactive visualizations, as well as a combination of both static and interactive visualizations, are becoming more common. Based on our results, we offer recommendations for future research as designers and researchers continue to improve the presentation of data visualizations in mHealth apps.

For nearly a century, electroencephalography (EEG) research has been a crucial tool in the study of brain activity, and this valuable research technique is becoming increasingly accessible due to recent advancements in technology. In this article, we report on a collaborative effort between cognitive neuroscientists and human–computer interaction (HCI) researchers to design, develop, and evaluate Sans Tracas – a cross-platform web application for running EEG experiments online. Using a multidisciplinary and user-centric iterative design approach, Sans Tracas was designed to be easy to use by researchers and study participants alike. To evaluate the feasibility of Sans Tracas for conducting online EEG experiments for people with varying EEG and Brain Computer Interface (BCI) knowledge, we conducted a study with 55 participants, followed by a semi-structured interview with 11 participants to uncover more qualitative insights into the usability and people’s perception of the platform. Results showed that the average number of trials to connect the Muse with Sans Tracas was 2.55 (SD = 3.42). The average time to complete the entire study was 17 minutes (SD = 4 minutes). Participants also found Sans Tracas fun to participate in EEG experiments independently and reported more interest in EEG and BCI research than before the study. We conclude that Sans Tracas is a usable platform for conducting online EEG studies and offers an alternative to traditional in-lab settings.

There is a rising interest in chatbots dedicated to enhancing sexual health. However, there is limited research on the effectiveness of these chatbots, and the current literature lacks sufficient exploration of gaps and patterns in this field. In this review, we provided an overview of the state-of-the-art research conducted on sexual health chatbots, with the goal of identifying prevalent trends, design patterns, and features. In addition, we investigated existing research gaps, challenges, and shortcomings in the landscape of sexual health chatbots. Further, we proposed potential enhancements and directions for future research and development to create more effective chatbots in this field. A systematic search and screening of the literature from the past decade (2013–2023), extracted from seven databases, yielded a total of 1040 studies, out of which 29 articles were included in the final review following screening. The findings suggest that chatbots are usable and effective tools in sexual health education, persuasion, and assistance that are appreciated for their confidentiality, efficiency, and 24/7 availability. However, their performance is hindered by limitations such as restricted scope of knowledge and challenges in understanding user inputs. Additionally, constraints such as text-only input/output modalities and a predominant reliance on the English language limit their accessibility and acceptability. There is also a crucial need for more research in low-income or lower-middle-income countries, where individuals require increased sexual health education and support.

The assessment of software application usability typically relies on a predefined set of general principles known as heuristics. However, these heuristics are often used interchangeably to evaluate games across different platforms such as smartphones, tablets, and desktops, potentially leading to inconsistent or inaccurate evaluations. Hence, there is a notable absence of a standard platform-centric heuristics to evaluate games for a particular platform. In this paper, we address this gap by developing 144 smartphone game heuristics (SmGH), spanning across six categories and accounting for technical, non-technical, and gameplay aspects. Further, we compared our proposed SmGH with four mobile game heuristics published in the literature. The aim of the comparison was to identify the overlaps and differences between SmGH and the existing heuristics in mobile game literature. Lastly, we conducted a preliminarily assessment of the utility of SmGH using gameplay analysis of 5 popular smartphone games (from 2017 to 2021, having 4.5+ average rating and 100 M + downloads) and 12 recent smartphone games (released in 2022). We obtained two important findings. First, there is a limited overlap among various mobile game heuristics in the literature. The first finding highlights an important takeaway to establish a standard set of platform-specific heuristics in both game user research and the industry. Second, popular games tend to incorporate a larger proportion of heuristics compared to recently released games. The second finding provide insights into the number and distribution of heuristics across all six categories within smartphone games, which will be beneficial for future evaluations of new and unseen games using SmGH. The second finding also suggest that adherence to platform-centric game heuristics may contribute to a game’s popularity on a particular platform and could be a factor considered by game developers. This work contributes to the field of Human Computer Interaction (HCI) and smartphone games by advancing our understanding and application of platform-centric game heuristics and highlights the significance of SmGH as a standard and reliable set of heuristics in the design and evaluation of smartphone games.

Suicide is a complex phenomenon because in addition to the individual who is impacted, its effects seep into many lives including caregivers of the individual experiencing suicidal thoughts and those who die by suicide. Often, caregivers seek help everywhere, but face various challenges including long wait times, difficulty accessing resources quickly, personal struggles with mental health, and time sensitivity. Mobile-health interventions are a promising solution for this, as they are easily accessible, available, geographic location independent and affordable. Many mobile apps for suicide prevention exist. However, not much is known about their design and efficacy, and if these apps address the unique mental health needs of caregivers dealing with suicidal individuals. To address these gaps, we designed LifeLink, a persuasive mobile application (app) specifically for supporting caregivers of individuals experiencing suicidal thoughts. Firstly, we reviewed 25 studies to determine risk factors that are most strongly associated with suicide and considerations for developing technological interventions for suicide prevention. Secondly, we reviewed 80 suicide prevention apps from app stores and academic literature to identify gaps and different persuasive strategies’ implementations using the Persuasive System Design (PSD) model. Thirdly, using the findings from the two systematic reviews, we designed a low-fidelity prototype of LifeLink app, implementing various evidence-based persuasive strategies. Next, using a user-centered design approach, 45 caregivers evaluated the user experience of LifeLink. Finally, we conducted a survey and semi-structured interviews to understand caregivers’ needs when supporting an individual, their assessment of perceived persuasiveness of the app and implemented strategies. Our results reveal that all the persuasive strategies were effective and significantly persuasive. LifeLink was found to be easy to use, engaging, useful, easy to navigate, elicited positive user experience, was helpful and impactful for caregivers. We also conducted a thematic analysis of participants’ qualitative feedback to uncover more insights. Findings from our evaluation demonstrate the potential of LifeLink as a valuable tool for caregivers that can increase mental health literacy and foster a supportive environment.

Mobile health (mHealth) apps are handy tools for tackling stigmatized mental health issues, including suicide. Mobile-based interventions for suicide prevention are easily accessible, increase the likelihood of honest reporting on sensitive topics and reduce stigma as compared to face-to-face or traditional interventions. Many mHealth apps for suicide prevention exist. However, the persuasive strategies employed in these apps and their efficacy remains unknown. To address this gap, we reviewed 80 suicide prevention apps available on app stores and in academic journals. We identified different persuasive strategies implemented in these apps using the Persuasive System Design (PSD) model. We also identified current trends within these apps, most and least-dominant implementations of persuasive strategies, effectiveness of apps, evaluation methods, and app content. We found that Personalization (n = 32) and Self-monitoring (n = 29) were the most-dominant strategies and Social Comparison, Social Role were the least-dominant strategies in suicide prevention apps. Based on our findings we discuss three major concerns in developing suicide prevention apps and offer recommendations for mitigating them. Our results show that persuasive strategies are a promising tool that can be used for designing suicide prevention apps. Our conclusions and recommendations will guide future work in suicide prevention app development and enhance the usability, effectiveness, and user-experience of such apps.

Persuasive games have emerged as effective tools for behaviour change across diverse life domains, engaging and motivating players towards positive behaviours. An essential factor that may influence the effectiveness of persuasive games is their framing, which refers to the strategic design and presentation of the game’s context, mechanics, and objectives. This paper investigates the role of game framing in enhancing the perceived effectiveness of persuasive games or not, using the context of a healthy eating game with three different game framing versions (gain-framing, loss-framing and gain-loss framing). In a study of 371 participants, our results show that while all the framing types were effective at promoting behaviour change, gain-framing was perceived as significantly less effective when compared to loss-framing and gain-loss framing. We also explore potential differences in the perceived effectiveness of four persuasive strategies (reward, competition, praise, and suggestion) implementations across the three framing types. We concluded by exploring the reasons behind these results and their implications for designing persuasive games for various framing types.

The importance of physical activity (PA) for overall health and well-being cannot be overstated, especially in today’s fast-paced and sedentary society. Engaging in enjoyable activities like dancing can significantly enhance PA levels and positively affect one’s mood. Advances in technology have the potential to increase individuals’ engagement in more physical activities. This work explores the effectiveness of augmented reality-driven persuasive intervention in enhancing users’ physical activity and mood. To achieve our goal, we developed AR Dancee, a mobile-driven intervention combining Augmented Reality, Machine Learning, and persuasive technology to encourage adults to increase their PA through dancing, ultimately improving their mood. A 15-day user study with 104 participants showed that the intervention effectively increased PA levels, with equal effectiveness across genders and a stronger impact on younger adults. The results also show that the intervention improved participants’ mood while reducing anxiety levels, demonstrating its potential for stress management. Overall, the contribution of this work to the HCI fields is threefold: (1) the design and development of an AR-driven persuasive mobile app, (2) providing design recommendations, and (3) pinpointing limitations and providing suggestions for future work.

In this study, we conducted a comprehensive evaluation of user reviews of suicide prevention apps using machine learning (ML) and thematic analysis to assess usability and user experience. Given the critical public health issue of suicide, mobile applications have emerged as potential intervention tools. However, user perspectives are underexplored. Our analysis encompassed user reviews from 39 suicide prevention apps, totaling 110,338 reviews. We employed natural language processing for sentiment analysis and implemented five ML classifiers with Random Forest achieving the highest F1 score (87.19%). The thematic analysis revealed seven negative themes including access to therapy and privacy concerns, alongside five positive themes such as app functionality and user engagement. This research quantitatively and qualitatively evaluates user sentiments, providing insights that can guide developers and policymakers in enhancing the design of suicide prevention apps, thereby addressing mental health challenges in the digital landscape.

Persuasive Technologies (PTs) are widely used for managing stress and improving well-being. PTs could contribute to the effort toward equality by making mental healthcare more accessible, even among underserved communities. However, most existing persuasive applications (apps) focus on designing for people in developed countries. Therefore, to address this gap, this paper presents the evaluation of iCare, a mobile health (mHealth) app for managing stress and improving well-being among an underserved population—the working-class Indian women. Specifically, we combined the power of mobile health and PTs to design the iCare app. To evaluate the effectiveness of iCare for stress management, 30 participants were recruited to use the app for two weeks and completed a post-test questionnaire about their experience followed by an optional interview with 22 participants to uncover additional insights. Quantitative questionnaire data was analyzed using descriptive and inferential statistics, while qualitative interview data was analyzed using a thematic analysis. Results showed that the iCare app was perceived as highly motivational, persuasive, and useful. Also, results show that using the iCare app brought significant positive changes by helping participants to better manage their stress and anxiety. We contribute to HCI research and practice by offering guidelines and insights for designing technologies for people from underserved communities.

People often congratulate and celebrate one another on social media, unknowingly disclosing critical information about themselves which may lead to privacy issues. Through quantitative, structural equation modeling, and thematic analytical methods used on our data of 700 Nigerian participants using snowball sampling, comprising 48.4% men (339) and 51.6% women (361), we assessed influences on birthday disclosures, emphasizing social gratification, social media usage, and knowledge of privacy settings. Despite a general understanding of privacy measures, many Nigerians prioritize the immediate rewards of social gratification. Prolonged platform usage tends to reduce birthday disclosure. Gender-based differences were notable: with increased social media use, Nigerian men disclosed birthdays less frequently than women. Identification of the unique considerations that Nigerians make before disclosing their birthday, including emotional and religious factors as well as consideration of the impact of disclosure on reputation, sets this study apart from similar previous studies in the Western context. The findings highlight the necessity of acknowledging regional cultural nuances in digital practices and support the call for region-specific digital literacy initiatives.

• This paper details the design, development, and evaluation of an mHealth application called “SmileApp,” which aims to promote positive emotions as a way to address stress. • A total of 101 participants used the app for 14 days, completed a questionnaire about their experience, and a subset of participants also participated in interviews to provide additional insights. • SmileApp uses artificial intelligence and persuasive technology to promote positive emotions. • Our findings offer valuable insights into how technology can enhance emotional well-being, with important implications for the design and development of future mHealth applications. Digital health technologies, particularly mobile health (mHealth) applications have been shown to address various needs, such as stress management and the promotion of positive health habits. In this research, we designed, developed, and evaluated an mHealth application called “SmileApp” to promote positive emotions as a means of addressing stress. SmileApp utilizes the advantages of artificial intelligence and persuasive technology and was created by incorporating well-established psychological theories and models. To evaluate the effectiveness of SmileApp , we conducted a 14-day within-subject study involving 101 participants in-the-wild. This is followed by an interview with 23 participants. Our results show that SmileApp can promote positive emotions using artificial intelligence and persuasive technology. Our findings underscore the importance of utilizing technology to support emotional wellbeing and lay the groundwork for further research and development in this area. The implications of this study demonstrate a paradigm shift in mHealth app design by introducing a new approach of promoting desired behaviors by encouraging users to read persuasive messages and play mobile games using their smile.

Emotion detection and Sentiment analysis techniques are used to understand polarity or emotions expressed by people in many cases, especially during interactive systems use. Recognizing users’ emotions is an important topic for human–computer interaction. Computers that recognize emotions would provide more natural interactions. Also, emotion detection helps design human-centred systems that provide adaptable behaviour change interventions based on users’ emotions. The growing capability of machine learning to analyze big data and extract emotions therein has led to a surge in research in this domain. With this increased attention, it becomes essential to investigate this research area and provide a comprehensive review of the current state. In this paper, we conduct a systematic review of 123 papers on machine learning-based emotion detection to investigate research trends along many themes, including machine learning approaches, application domain, data, evaluation, and outcome. The results demonstrate: 1) increasing interest in this domain, 2) supervised machine learning (namely, SVM and Naïve Bayes) are the most popular algorithms, 3) Text datasets in the English language are the most common data source, and 4) most research use Accuracy to evaluate performance. Based on the findings, we suggest future directions and recommendations for developing human-centred systems.

Fun and social affiliation are good predictors of long-term intention to use game-based interventions including those for motivating physical activity, yet current player matching algorithms are poor at facilitating social connectedness. In this paper, we report on the results of a study investigating how different player traits are associated with interest in social features of an exercise game for improving player experience through better player matching using common and complementary characteristics. Twelve conceptual scenarios were illustrated using storyboards and data was collected from 196 respondents who rated their attitudes and preferences towards gamification elements. Correlational results showed that all scenarios, except for cutting corners, were perceived as persuasive, enjoyable, engaging and are likely to increase future exercise intention for players who score high on Philanthropist- and Socialiser-oriented traits. Results also showed that many players favour the altruistic donation feature. Furthermore, qualitative results underscore that it is the player's partner that matters more than the players’ personalities. We conclude with practical recommendations for designing more personalised exercise games that can include more socially engaging game mechanics in the future.

Persuasive gamified systems are effective tools for motivating behaviour change using various persuasive strategies. In line with the reinforcement theory, some persuasive gamified systems employ reward and punishment in their design to achieve the intended behavioural outcome. Research has argued both in favour and against using these strategies in behaviour change applications due to mixed results with respect to their effectiveness. However, there is a lack knowledge about how interventions using these strategies could motivate or demotivate behaviours. Therefore, this paper explores the mechanism through which Reward and Punishment motivate or demotivate behaviours with respect to their strengths and weaknesses. The results of large-scale exploratory studies (N = 1768) uncover important strengths and weaknesses that could facilitate or hinder the effectiveness of Reward and Punishment at motivating behaviour change. These include their ability to engage users and make behaviour fun, reinforce commitments to goals, and reveal some consequences of bad behaviour. We also compared the perceived effectiveness of reward and punishment quantitatively.

Despite the growing number of mHealth apps for tracking and helping users to form and sustain health habits, most apps are not evidence-based and are not evaluated by the users to uncover potential issues and determine effectiveness. To fill this gap, we used a mixed methods approach to evaluate a mood-self-tracking app called Feeling Moodie. Data was collected from 34 participants [age range: 18–55 years old, with 15/34 (44%) being between the ages 26 and 35 years old; sex: 17 males and 17 females] who used the app for 15 days and completed a questionnaire about their experience followed by an interview with 18 participants to uncover more qualitative insights. Results showed a positive range for attractiveness, perspicuity, efficiency, dependability, and stimulation, but not for novelty which suggests that Feeling Moodie can be improved by increasing the level of creativity to further captivate the user’s interest. Furthermore, interviews revealed that while some participants expressed doing mood check-ins felt like a “chore,” others reported that at first, they had to use it intentionally, but after a while, it became a “rhythm,” pulling them to the experience. Based on the insights, we offer practical guidelines for increasing the level of interactivity and gradually guiding the user by using a variety of features to help them to form good habits. The results obtained in this work can inform designers on how to design more personalized apps and increase the possibility that the app will be adopted. The article contributes to a better understanding of the emotional and technological implications for designing and improving the quality of mood-tracking apps.

The use of smartphones has become an integral part of everyone's lives. Due to the ubiquitous nature and multiple functionalities of smartphones, the data handled by these devices are sensitive in nature. Despite the measures companies take to protect users’ data, research has shown that people do not take the necessary actions to stay safe from security and privacy threats. Persuasive games have been implemented across various domains to motivate people towards a positive behaviour change. Even though persuasive games could be effective, research has shown that the one-size-fits-all approach to designing persuasive games might not be as effective as the tailored versions of the game. This paper presents the design and evaluation of a persuasive game to improve user awareness about smartphone security and privacy tailored to the user's motivational orientation using Regulatory Focus Theory. From the results of our mixed-methods in-the-wild study of 102 people followed by a one-on-one interview of 25 people, it is evident that the tailored version of the persuasive game performed better than the non-tailored version of the game towards improving users’ secure smartphone behaviour. We contribute to the broader HCI community by offering design suggestions and the benefits of tailoring persuasive games.

The workshop aims to discuss open problems, challenges, and innovative research approaches in the area of persuasive and behavior change recommender systems, that is, recommender systems aimed at modifying people's habits and behavior. Some questions that motivate this workshop are: What kind of theory is more suitable to inform the design of behavior change recommender systems? What kind of personal data (e.g., coming from environmental sensors, wearable devices, etc.) should we use to design behavior change recommendations? How should we deliver them (i.e., what kind of communication channels and interfaces should we use)? What kind of strategies should we implement to design timely and contextualized recommendations? How can we support the user's motivation to adhere to the recommendations provided? How can we “persuade” users in the long term?

The growing number of people with mental health issues and the worldwide shortage of professionals emphasise the need for tailored persuasive interventions. This paper explores the relationships between the types of motivation individuals experience and their preferences for various features that are widely used in persuasive apps for mental and emotional well-being. First, we reviewed 103 mental health apps from app stores and identified various persuasive features and then conducted focus-group studies of 32 participants. Finally, we implemented the common features in persuasive mental health app prototypes and conducted a large-scale study of 561 users to evaluate their perceived effectiveness. The results reveal that people’s motivation types significantly influence the perceived persuasiveness of different features. People high in intrinsic motivation are more motivated by apps that offer relaxation exercises while providing opportunities to track various mental health-related information. We offer design guidelines for tailoring persuasive mental health apps based on motivation types.

Persuasive systems motivate behaviour change using persuasive strategies (PS) which are often implemented in various ways. However, whether or not the effectiveness of PS varies depending on implementation choices is yet to be investigated via an empirical study. We conduct a large-scale study of 568 participants to investigate if and how individuals at different Stages of Change (SoC) respond to different implementations of each strategy in the same system. We also explore why the implementations motivate behaviour change using ARCS motivation model. Our results show that people’s SoC plays a significant role in the perceived effectiveness of different implementations of the same strategy and that the implementations motivate for different reasons. For instance, people at the precontemplation stage are motivated by reward strategy implemented as badges because it increases their Confidence, while people in the preparation stage prefer reward implemented as points to build their Confidence. Our work links SoC theory with motivation theory and Persuasive Systems Design (PSD) model to offer practical guidelines for tailoring PS implementations to individuals to motivate behaviour change.

Persuasive games are widely implemented in the healthcare domain to promote behaviour change among individuals. Previous research shows that using persuasive games increases motivation and awareness, leading to a positive change in behaviour. However, there is little knowledge on which persuasive strategies will motivate people at different Stages of Behaviour Change and whether tailoring persuasive games to match users’ stages of change will increase their effectiveness with respect to their motivational appeal towards promoting disease awareness and prevention using the ARCS motivation scales and their intention to adopt the precautionary measures. To address this gap, using COVID-19 as a case study, we designed two different versions of a persuasive game, called COVID Pacman, using different persuasive strategies. The two versions of the game target the same goal of motivating the adoption of precautionary measures. We conducted a quantitative study (N=127) followed by semi-structured interviews of 18 participants. The results of conducting an ANOVA on the quantitative data and thematic analysis on the qualitative study show that tailoring the persuasive games to individual’s stages of change by using appropriate persuasive strategies increased their effectiveness with respect to their ability to motivate people to adopt the precautionary measures towards disease prevention compared to the non-tailored version.

Unhealthy lifestyle behaviors such as spending too many hours sitting and inadequate physical activity (PA) can contribute to different chronic diseases. Research has revealed the capabilities of digital technology interventions such as persuasive technologies (PTs) for providing health support and encouraging healthy behavior changes to assist people in preventing chronic diseases and having healthier lifestyles. Thus, the use of mobile technology to deliver PT interventions has dramatically increased, especially for promoting PA and reducing sedentary behavior (SB) by employing various persuasive strategies (PSs). This paper provides a systematic review of 16 years of research from 2006 to 2021. The review aims to (1) explore the various ways each strategy is implemented on mobile-based PTs for PA and SB, (2) evaluate the effectiveness of different ways of implementing the PSs on mobile-based PT interventions for PA and SB, (3) provide a comparison of the different ways of implementing each PS, (4) show the weaknesses and strengths of the interventions based on the strategies and implementations, (5) highlight the limitations and pitfalls of the existing research, and (6) give recommendations and directions for future research.

Traditional health systems mostly rely on rules created by experts to offer adaptive interventions to patients. However, with recent advances in artificial intelligence (AI) and machine learning (ML) techniques, health-related systems are becoming more sophisticated with higher accuracy in providing more personalized interventions or treatments to individual patients. In this paper, we present an extensive literature review to explore the current trends in ML-based adaptive systems for health and well-being. We conduct a systematic search for articles published between January 2011 and April 2022 and selected 87 articles that met our inclusion criteria for review. The selected articles target 18 health and wellness domains including disease management, assistive healthcare, medical diagnosis, mental health, physical activity, dietary management, health monitoring, substance use, smoking cessation, homeopathy remedy finding, patient privacy, mobile health (mHealth) apps finder, clinician knowledge representation for neonatal emergency care, dental and oral health, medication management, disease surveillance, medical specialty recommendation, and health awareness. Our review focuses on five key areas across the target domains: data collection strategies, model development process, ML techniques utilized, model evaluation techniques, as well as adaptive or personalization strategies for health and wellness interventions. We also identified various technical and methodological challenges including data volume constraints, data quality issues, data diversity or variability issues, infrastructure-related issues, and suitability of interventions which offer directions for future work in this area. Finally, we offer recommendations for tackling these challenges, leveraging on technological advances such as multimodality, Cloud technology, online learning, edge computing, automatic re-calibration, Bluetooth auto-reconnection, feedback pipeline, federated learning, explainable AI, and co-creation of health and wellness interventions.

Brain-computer interactions allow humans to passively or actively control computer systems using their brain activity. For more than a decade now, these interactions have been implemented and evaluated in immersive virtual environments where they prompt novel means of human interaction with systems. In this paper, we present a systematic review of 76 studies published over the last 10 years that develop and evaluate immersive virtual reality or augmented reality systems with electroencephalography-based interactions. The aim of the review is to summarize and highlight trends in technology design, research methods, current practices, techniques used in systems of this kind, challenges and opportunities that present direction for future research in this area. Our analysis uncovers useful insights, limitations, and highlights of the trends, innovations, and usability and technical challenges at the intersection of brain-computer interfaces and immersive technologies, as well as recommendations for future research.

The privacy calculus framework and trust heuristics has been used to understand people’s privacy decision-making processes. However, most existing studies are mainly focused on people from developed countries. In this study, we use the privacy calculus in combination with trust heuristics to analyse how people from a developing African nation make decisions. Specifically, we conduct a web-based experiment in which 232 participants from Nigeria used a financial planning prototype app to respond to a number of disclosure questions. We examined how their perceived benefit, perceived sensitivity, and trust in the app influenced their disclosure decisions. In addition, we investigated possible moderating effects of gender and used Partial Least Squares path modelling to analyze our data. Our results show that perceived sensitivity (risks) and perceived benefits influenced the decision-making process of our participants. In addition, women were more likely to change their perception of sensitivity and benefits based on trust, while men were more likely to disclose information based on their perception of benefits. We also found that women were less likely to disclose their information to the app than men. Based on our findings, we make recommendations for educators, financial institutions, designers, and policymakers that aim to raise privacy awareness and design interventions in Nigeria and Africa at large.

Games for change is a growing research field and studies have shown that these games can promote positive behavior change using various persuasive strategies. This article presents a systematic review of 130 persuasive games from the literature published in the last 21 years (2001–2021) to 1) highlight the current trends in the field with respect to domains, year, country, technology platforms, and genre; 2) identify what strategies are employed in the games and their comparative analysis across domains; 3) uncover various ways the persuasive strategies are operationalized in games; 4) explore for possible relationships between persuasive games effectiveness and the number of strategies employed; and 5) highlight gaps and opportunities for future research in the area of persuasive games. Our analysis reveals therewardstrategy is the most popular persuasive strategy employed in the persuasive games’ research. We also uncovered that, even though persuasive games have been strongly effective at promoting behavior change, there was a significant negative relationship between the number of persuasive strategies employed in persuasive games and their overall effectiveness. Based on these findings, we provide insights and design suggestions, operationalization, and assessment for persuasive games.

Robot-assisted intravascular interventions have evolved as unique treatments approach for cardiovascular diseases. However, the technology currently has low potentials for catheterization skill evaluation, slow learning curve, and inability to transfer experience gained from manual interventions. This study proposes a new weighting-based deep ensemble model for recognizing interventionalists' hand motions in manual and robotic intravascular catheterization. The model has a module of neural layers for extracting features in electromyography data, and an ensemble of machine learning methods for classifying interventionalists' hand gestures as one of the six hand motions used during catheterization. A soft-weighting technique is applied to guide the contributions of each base learners. The model is validated with electromyography data recorded duringin-vitroandin-vivotrials and labeled asmany-to-onesequences. Results obtained show the proposed model could achieve 97.52% and 47.80% recognition performances on test samples in thein-vitroandin-vivodata, respectively. For the latter, transfer learning was applied to update weights from thein-vitrodata, and the retrained model was used for recognizing the hand motions in thein-vivodata. The weighting-based ensemble was evaluated against the base learners and the results obtained shows it has a more stable performance across the six hand motion classes. Also, the proposed model was compared with four existing methods used for hand motion recognition in intravascular catheterization. The results obtained show our model has the best recognition performances for both thein-vitroandin-vivocatheterization datasets. This study is developed toward increasing interventionalists' skills in robot-assisted catheterization.

Twitter is one of the popular social network platforms used by both humans and bots to share information, and to distribute misinformation or disinformation. The goal of this research is to explore state-of-the-art Twitter bot detection systems: Botometer, and Tweetbotornot. In doing so, we aim to understand their characteristics, similarities, and differences as well as to identify additional methods to improve their performances. Evaluations performed on 15 datasets show that the proposed methods used as add-ons to Botometer were able to improve the detection of bot/human accounts on Twitter using the simple characteristics of Twitter accounts.

Augmented Reality (AR) and Virtual Reality (VR) have shown potential benefits in managing healthy behavior. This paper presents a systematic review of AR- or VR-driven interventions for promoting healthy behaviors. The review investigates the design, implementations of the intervention, persuasive strategies, intervention platforms, underlying technologies, current trends, and research gaps. Our review of the past 10-years' work in the area reveals that 1) the considered papers focused on seven main healthy behaviors, where “alcohol use” emerged as the most commonly considered behavior; 2) trustworthiness emerged as the most commonly used persuasive strategy; 3) youth are the most targeted audience; 4) VR is more common than AR; and 5) most AR- or VR-driven interventions are perceived to be effective in motivating healthy behavior in people. We also uncover how they use Artificial Intelligence and Object Tracking in this space. Finally, we identify gaps and offer recommendations for advancing research in this area.

Mobile health (mHealth) apps show potential contributions as interactive systems for managing users' health conditions. They are also used to improve health habits using behaviour change strategies. However, the trends, effectiveness, and design practices of these apps in terms of behaviour change are unclear yet. With a collaboration between researchers, domain experts, interactive systems developers and professionals, this paper aims to fill this gap by systematically investigating 70 mHealth apps using two popular behaviour change frameworks, namely App Behaviour Change Scale (ABACUS) and the Persuasive System Design (PSD) model. The study investigates the most common strategies and how these strategies were designed and implemented in the apps to achieve the targeted design objectives. Furthermore, the study evaluates apps' behaviour change potential using the behaviour Change Score (BCS), a measure we introduced to evaluate how the apps employ behaviour change strategies. The results show that 1) Journaling is the most common category of apps. 2) the most employed strategies are Self-monitoring, Customize and Personalize, and Reminders. And 3) there is a positive correlation between apps' ranks (based on ratings and installation) and the BCS score of most strategies. Based on our findings, we offer recommendations for designing and developing mHealth apps and present opportunities for future work in this area.

Over the years, there has been a global increase in the use of technology to deliver interventions for health and wellness, such as improving people's mental health and resilience. An example of such technology is the Q-Life app which aims to improve people's resilience to stress and adverse life events through various coping mechanisms, including journaling. Using a combination of sentiment analysis and thematic analysis methods, this paper presents the results of analyzing 6023 journal entries from 755 users. We uncover both positive and negative factors that are associated with resilience. First, we apply two lexicon-based and eight machine learning (ML) techniques to classify journal entries into positive or negative sentiment polarity, and then compare the performance of these classifiers to determine the best performing classifier overall. Our results show that Support Vector Machine (SVM) is the best classifier overall, outperforming other ML classifiers and lexicon-based classifiers with a high F1-score of 89.7%. Second, we conduct thematic analysis of negative and positive journal entries to identify themes representing factors associated with resilience either negatively or positively, and to determine various coping mechanisms. Our findings reveal 14 negative themes such as stress, worry, loneliness, lack of motivation, sickness, relationship issues, as well as depression and anxiety. Also, 13 positive themes emerged including self-efficacy, gratitude, socialization, progression, relaxation, and physical activity. Seven (7) coping mechanisms are also identified including time management, quality sleep, and mindfulness. Finally, we reflect on our findings and suggest technological interventions that address the negative factors to promote resilience.

Persuasive systems (PS) are effective at motivating behaviour change using various persuasive strategies. Research shows that tailoring these systems increases their effectiveness. However, there is little knowledge on how PS can be tailored to people's Stages of Change (SoC). We conduct a large-scale study of 568 participants to investigate how individuals at different SoC respond to various strategies. We also explore why the strategies motivate behaviour change using the ARCS motivation model. Our results show that people's SoC plays a significant role in the perceived persuasiveness of different strategies and that the strategies motivate for different reasons. For instance, people at the precontemplation stage tend to be strongly motivated by self-monitoring strategy because it raises their consciousness or self-awareness. Our work is the first to link research on the theory of SoC with the theory of motivation and Persuasive Systems Design (PSD) model to develop practical guidelines to inform the tailoring of persuasive systems.

Increased physical activity has been shown to reduce morbidity and mortality among adults. Over the years, mobile apps have been developed to encourage people to engage in physical activity, such as walking or running, by employing various persuasive strategies. However, the choice of these strategies is often based on designers' intuition without knowing if the strategies will be effective for target audience and the target behaviour. To address this gap, we conduct a study with 103 adults to assess the perceived effectiveness of 12 widely used strategies in health games design. The strategies are based on the Persuasive Systems Design (PSD) framework. Our results reveal that the strategies are effective for promoting physical activity at varying degrees. These results inform the development of the game, called TreeCare. Next, we conduct a 3-week field study involving 23 target users to evaluate the game in terms of effectiveness and usability. Our results show that TreeCare significantly improved users' physical activity levels. In addition, the game is found to be easy to use, engaging, aesthetically pleasing, and enjoyable. We reflect on our findings and offer practical guidelines to inform the design of effective and usable persuasive applications.

In this research, using storyboard implementation, we investigate the relationship between social game elements and the Hexad user type model for motivating exercise and play. The goal is to see if and how different gamer types are associated with interest in social features and if they can be used as the basis for player compatibility matching (similar or complementary). Participants (N = 60) rated the level of perceived persuasiveness, enjoyment, social engagement, and intention for future play on 8 social elements for step-based asynchronous exergames. Preliminary results show a significant positive relationship between social game elements and player types. Correlational analyses demonstrated that the gamification elements “Sharing Knowledge”, “Supportive Updates”, “Rotating Leadership” and “Leaderboard” were most strongly related to the “Philanthropist” player type. We conclude with recommendations for designing personalized multiplayer mobile exergames.

Physical inactivity is a significant risk factor for many non-communicable diseases such as heart disease, diabetes, and evidence shows that physical inactivity is one of the highest risk factors for death globally. Research has shown that theory-driven persuasive interventions are more effective at promoting behaviour change than generic ones. However, research on the determinants of physical activity and the moderating effect of age and gender among non-Western culture is limited. To close this gap, we conduct a large-scale study of the determinants of physical activity among 217 participants from Saudi Arabia using the extended Health Belief Model (HBM), a commonly applied behavioural model in health interventions design. We also assessed for the moderating effect of age and gender. Our findings show that Social influence, Cue to action and Perceived severity are the strongest determinants of physical activity in Saudi adults. We map these determinants to their corresponding persuasive strategies that can be used in operationalizing them in persuasive applications for promoting physical activity. Finally, we discuss the implication of our findings and offer design guidelines for persuasive interventions that appeal to both a broad audience and tailored to a particular group depending on their gender and age group.

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making. In addition, experiments demonstrated that the research model achieved an accuracy of 81.15% - a higher accuracy than that of several other well-known machine-learning algorithms for COVID-19-Sentiment Classification.

Diabetes is a non-communicable disease associated with increased level of glucose due to inadequate supply of insulin (known as Type 1 diabetes) or inability to use insulin efficiently (known as Type 2 diabetes). Though the exact cause of Type 1 diabetes is unknown, the probable causes are genetics and environmental factors (such as exposure to viruses). On the other hand, Type 2 diabetes is largely linked to unhealthy lifestyle choices. In Nigeria, many people are believed to be living with diabetes and the country's diabetes prevalence rate is one of the highest in Africa. To determine the factors responsible for diabetes prevalence in Nigeria, we analyzed social media contents related to diabetes since billions of people, including diabetic patients and healthcare professionals, use social media platforms to freely share their experiences and discuss many health-related topics. None of the existing research targets the African audience who are also major users of social media platforms; hence our work aims to close this gap by leveraging an African social media platform targeted at Nigerians to gather diabetes-related data, and then applying machine learning technique to detect those factors responsible for diabetes prevalence in Nigeria. Based on our results, we discussed positive behavioural or lifestyle changes that are necessary to prevent and treat diabetes in Nigeria, as well as intervention designs required to bring about those changes. Future work will develop a diabetes intervention application implementing all the design features highlighted in Section V of this paper and making it generally accessible to Nigerians.

This full paper in the research category investigates the core competencies required in Computer and Information Systems (CIS) education. We define competency, in the context of existing literature, to mean skills, knowledge and disposition required in a given profession. Skills can be hard skills or soft skills. Insight and awareness of competencies are core imperatives in curriculum design and also play a critical role in the development of academic programs, courses, and syllabi. Despite their importance, there are limited studies that investigate and provide the core Information Systems (IS) competencies especially from an industry perspective. The research question at hand is: What core competencies are required in a Computer and Information Systems program from an industry perspective' To answer this question, this paper conducts a thematic analysis to analyze 132 CIS-related-industry job descriptions collected from five currently-popular job portals (indeed.com, monster.com, careerBuilder.com, idealist.org and simplyhired.com). We analyzed this data to contribute a set of competencies that can support academic endeavors, such as curriculum revision in the Computer Information Systems profession. Furthermore, we provide a classification of those competencies into knowledge, skills and disposition. CIS instructors and academic program developers can use the findings of this research to facilitate the development CIS-related academic-program outcomes, learning objectives and curricula. Our findings can also be used to facilitate competency-based learning in the CIS profession.

The Internet of Things (IoT) is inspired by network interconnectedness of humans, objects, and cloud services to facilitate new use cases and new business models across multiple enterprise domains. This creates the need for continuous data streaming in IoT architectures which are mainly designed following the broadcast model. As more devices communicate with each other via the Internet, it will be crucial to determine the origins of requests and responses; especially in situations where trust is paramount such as wearable IoT. It is however difficult to determine data origins in IoT ecosystems due to the inherent sporadic nature of wireless networks. Also, multiple IoT devices can be generating similar content and it is important to identify individual data sources transparently. Some previous works focus on the request perspective and employed provenance techniques to determine data sources. However, some of these solutions are not robust for a complete message and data exchanges; especially in a request-response scenario where IoT devices are involved. Thus, this paper proposes a combination of policy-based provenance and modelled the peer-to-peer IoT device communication as a graph network. Using Floyd's algorithm, we are able to determine data origins in shortest paths between interconnected IoT devices.

The success of persuasive systems in changing people's attitudes and behaviours has been established in various domains. Specifically, research has shown that personalized persuasive technology is more effective at achieving the desired goal than the one-size-fits-all approach. However, in the education domain, there are limited studies on the personalization of persuasive strategies to students. To advance persuasive technology research in this area, we investigated the susceptibility of undergraduate students (n = 243) to four persuasive strategies (Reward, Competition, Social Comparison and Social Learning) in order to provide a guideline for designing and personalizing persuasive systems in education. These four strategies were chosen because research on persuasion has established their effectiveness in changing behaviour and/or attitude. The results of our analysis reveal that students are more susceptible to Reward, followed by Competition and Social Comparison (both of which come in the second place) and Social Learning (the least persuasive). Moreover, there is no gender difference in the persuasiveness of the strategies. Therefore, in choosing persuasive strategies to motivate student's learning and success, among the strategies we investigated, Reward should be given priority, followed by Competition and Social Comparison, while Social Learning should be least favoured.

Persuasive technologies (PTs) motivate behaviour change using various persuasive strategies. However, there is still a dearth of knowledge on how PTs motivate behaviour change and how to design systems to increase their persuasiveness. To provide empirical insight into the mechanism through which PTs persuade, we conducted a large-scale study with 543 participants to investigate the relation between Attention, Relevance, Confidence, and Satisfaction constructs from the ARCS model of motivation and 10 strategies that are commonly used in persuasive systems design. Our results show that the ARCS constructs collectively explain between 82% and 91% of the variance in persuasiveness across the ten strategies. Relevance, followed by Attention, has the strongest association with persuasiveness. The result generalises across gender groups. Therefore, to increase a system’s persuasiveness, designers should focus on designing to increase relevance and to capture user’s attention, while also promoting confidence and a feeling of satisfaction. We contribute to Human–Computer Interaction (HCI) and Persuasive Technology by offering design guidelines for PTs to increase their persuasiveness and hence efficacy.

Persuasive gameful systems are effective tools for motivating behaviour change. Research has shown that tailoring these systems to individuals can increase their efficacy; however, there is little knowledge on how to personalize them. We conducted a large-scale study of 543 participants to investigate how different gamification user types responded to ten persuasive strategies depicted in storyboards representing persuasive gameful health systems. Our results reveal that people's gamification user types play significant roles in the perceived persuasiveness of different strategies. People scoring high in the 'player' user type tend to be motivated by competition, comparison, cooperation, and reward while 'disruptors' are likely to be demotivated by punishment, goal-setting, simulation, and self-monitoring. 'Socialisers' could be motivated using any of the strategies; they are the most responsive to persuasion overall. Finally, we contribute to CHI research and practice by offering design guidelines for tailoring persuasive gameful systems to each gamification user type.

Persuasive Educational Technology (PET) is an effective tool for engaging students and promoting learning, using various persuasive strategies. Research has shown that personalizing PET could increase their effectiveness at engaging students and promoting learning. Cognitive ability has been identified as an important factor that could lead to individual differences in learning. Yet, there is a gap in knowledge on how learners' ability on various cognitive dimensions identified by Bloom's Taxonomy, is associated with their susceptibility to distinct persuasive strategies often used in PETs designs. To fill this gap, we conducted a large-scale study of 402 students to investigate how their cognitive ability scores relate to their susceptibility to persuasive strategies (Reward, Social Learning, and Trustworthiness) that are commonly used in PET design. We assessed the Cognitive ability scores using the test questions adapted from the Educational Testing Service (ETS) Kit. Our results show that there is a relationship between individuals' cognitive abilities and their susceptibility to persuasive strategies. People with high ability to Remember and Evaluate concepts are more susceptible to the Reward Strategy, while those high in ability to Understand are positively associated with the Trustworthiness Strategy. Similarly, we uncovered that individuals high in the ability to Apply, Analyze, and Synthesize ideas are positively associated with the Social Learning Strategy. These findings can guide PET designers on how to develop PET tailored to learners belonging to different cognitive ability dimensions.

Requirements engineering (RE) provides a set of techniques and tools to, `EASV' (elicit, analyze, specify, and validate) the capabilities a product must have in order to meet user needs, solve definite user problems and deliver expected values to users. RE courses are usually designed to focus on software and computer hardware products, and are thus taught in software engineering (SE), information systems (IS) and computer science (CS). But RE tools and techniques are also intrinsically applicable and essential to other engineering disciplines, since those disciplines also develop and deploy products. For instance, products can be a motor control center (MCC) developed by electric engineers or aircraft engines developed by mechanical engineers. Successful development of any of these products requires techniques to precisely ESAV user needs and capabilities required to satisfy those needs. Therefore RE should be an essential component of engineering curriculum. This panel aims to bring together faculty and practitioners from engineering and computer science disciplines to identify and discuss challenges, benefits, and strategies for integrating RE course into engineering curriculum.

Behavior modeling has become a very important behavior change technique employed in most fitness apps. However, its effect as a persuasive strategy on users has not been well investigated. Consequently, we conducted an empirical study among 669 participants to uncover: (1) how the perceived persuasiveness of behavior model design influences three social cognitive theory (SCT) determinants of behavior: self-efficacy, self-regulation and outcome expectation; and (2) the moderating effect of gender-based personalization. We based our study on user evaluation of prototypes of behavior models performing push-up and squat exercise behaviors as a case study. Our results show that, overall, the perceived persuasiveness of behavior models significantly influences all of the three SCT factors. The effect of persuasiveness on self-regulation (β = 0.42, p < 0.001) and outcome expectation (β = 0.41, p < 0.001) is stronger than on self-efficacy (β = 0.13, p < 0.05). Moreover, the behavior model design has a stronger effect on females' self-efficacy and males' outcome expectation if personalized to their gender. We discuss the implication of our findings.

Personalizing persuasive technologies (PTs) is one of the hallmarks of a successful PT intervention. However, there is a lack of understanding of how Africans and North Americans differ or are similar in the susceptibility to persuasive strategies. To bridge this gap, we conducted a cross-cultural study among 284 subjects to investigate the moderating effect of culture on the susceptibility of users to Cialdini's principles of persuasion. Specifically, using Nigeria and Canada as a case study, we investigated how both groups vary in their levels of susceptibility to Authority, Commitment, Consensus, Liking, Reciprocity and Scarcity. The results of our analysis show that Nigerians are more susceptible to Authority and Scarcity than Canadians, while Canadians are more susceptible to Reciprocity, Liking and Consensus than Nigerians. However, both groups do not differ with respect to Commitment (the most persuasive strategy). Finally, we discussed our findings and mapped the most persuasive Cialdini's principles in each group to implementable persuasive strategies in the PT domain.

Typologies for understanding players’ preferences toward different gameplay styles have gained popularity in research. However, attempts to model players’ preferences are based on type models instead of trait models, contrary to the latest personality research. One such model, BrainHex, was designed as an interim model to enable investigations toward a definitive player trait model. However, it lacks empirical validation in support of its psychometric properties. The present work analyzed a dataset with over 50,000 respondents to devise a player traits model based off the BrainHex scale. Results indicate three player traits: action, esthetic, and goal orientation. Furthermore, we analyzed the games listed by participants as examples of what they enjoy, to understand which factors influence player preferences. Results illustrate that the emergent player traits and participants’ genders and attitudes toward story can partially explain player preferences toward certain games. Finally, we present the implications toward a definitive player traits model.

Persuasive games and gamified systems are effective tools for motivating behavior change using various persuasive strategies. Research has shown that tailoring these systems can increase their efficacy. However, there is little knowledge on how game-based persuasive systems can be tailored to individuals of various personality traits. To advance research in this area, we conducted a large-scale study of 660 participants to investigate how different personalities respond to various persuasive strategies that are used in persuasive health games and gamified systems. Our results reveal that people's personality traits play a significant role in the perceived persuasiveness of different strategies. Conscientious people tend to be motivated by goal setting, simulation, self-monitoring and feedback; people who are more open to experience are more likely to be demotivated by rewards, competition, comparison, and cooperation. We contribute to the CHI community by offering design guidelines for tailoring persuasive games and gamified designs to a particular group of personalities.

There has been a continuous increase in the design and application of computer games for purposes other than entertainment in recent years. Serious games—games that motivate behavior and retain attention in serious contexts—can change the attitudes, behaviors, and habits of players. These games for change have been shown to motivate behavior change, persuade people, and promote learning using various persuasive strategies. However, persuasive strategies that motivate one player may demotivate another. In this article, we show the importance of tailoring games for change in the context of a game designed to improve healthy eating habits. We tailored a custom-designed game by adapting only the persuasive strategies employed; the game mechanics themselves did not vary. Tailoring the game design to players’ personality type improved the effectiveness of the games in promoting positive attitudes, intention to change behavior, and self-efficacy. Furthermore, we show that the benefits of tailoring the game intervention are not explained by the improved player experience, but directly by the choice of persuasive strategy employed. Designers and researchers of games for change can use our results to improve the efficacy of their game-based interventions.

People using wheelchairs face barriers in their daily lives, many of which are created by people who surround them. Promoting positive attitudes towards persons with disabilities is an integral step in removing these barriers and improving their quality of life. In this context, persuasive games offer an opportunity of encouraging attitude change. We created a wheelchair-controlled persuasive game to study how embodied interaction can be applied to influence player attitudes over time. Our results show that the game intervention successfully raised awareness for challenges that people using wheelchairs face, and that embodied interaction is a more effective approach than traditional input in terms of retaining attitude change over time. Based on these findings, we provide design strategies for embodied interaction in persuasive games, and outline how our findings can be leveraged to help designers create effective persuasive experiences beyond games.

Persuasive games are an effective approach for motivating health behavior, and recent years have seen an increase in games designed for changing human behaviors or attitudes. However, these games are limited in two major ways: first, they are not based on theories of what motivates healthy behavior change. This makes it difficult to evaluate why a persuasive approach works. Second, most persuasive games treat players as a monolithic group. As an attempt to resolve these weaknesses, we conducted a large-scale survey of 642 gamers' eating habits and their associated determinants of healthy behavior to understand how health behavior relates to gamer type. We developed seven different models of healthy eating behavior for the gamer types identified by BrainHex. We then explored the differences between the models and created two approaches for effective persuasive game design based on our results. The first is a one-size-fits-all approach that will motivate the majority of the population, while not demotivating any players. The second is a personalized approach that will best motivate a particular type of gamer. Finally, to make our approaches actionable in persuasive game design, we map common game mechanics to the determinants of healthy behavior.