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.

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.

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.

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.

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.

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.

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.