The development of technology, such as the Internet of Things and artificial intelligence, has significantly advanced many fields of study. Animal research is no exception, as these technologies have enabled data collection through various sensing devices. Advanced computer systems equipped with artificial intelligence capabilities can process these data, allowing researchers to identify significant behaviors related to the detection of illnesses, discerning the emotional state of the animals, and even recognizing individual animal identities. This review includes articles in the English language published between 2011 and 2022. A total of 263 articles were retrieved, and after applying inclusion criteria, only 23 were deemed eligible for analysis. Sensor fusion algorithms were categorized into three levels: Raw or low (26%), Feature or medium (39%), and Decision or high (34%). Most articles focused on posture and activity detection, and the target species were primarily cows (32%) and horses (12%) in the three levels of fusion. The accelerometer was present at all levels. The findings indicate that the study of sensor fusion applied to animals is still in its early stages and has yet to be fully explored. There is an opportunity to research the use of sensor fusion for combining movement data with biometric sensors to develop animal welfare applications. Overall, the integration of sensor fusion and machine learning algorithms can provide a more in-depth understanding of animal behavior and contribute to better animal welfare, production efficiency, and conservation efforts.

Background: Childhood obesity has risen dramatically in recent decades, reaching epidemic levels. Children need guidance on and support for maintaining a healthy diet and physical activity to ensure that they grow appropriately and develop healthy eating habits. Serious video games have shown positive effects on promoting the nutritional knowledge, and eating attitudes and behaviors of children; however, research about the usefulness of such games with younger children (8-10 years old) is sparse. Objective: The objective of this study was to design and test the serious video game FoodRateMaster targeting children between 8 and 10 years old. The game includes nutritional information and behavior change techniques to help children improve their knowledge of healthy and unhealthy foods, increase their intake of healthy food, and reduce their intake of ultraprocessed food. In addition, FoodRateMaster was designed as an active game to promote physical activity. Methods: An interdisciplinary team developed FoodRateMaster following an iterative methodology based on a user-centered design. A total of 60 participants (mean age 9 years, SD 0.8; 53% male) completed 12 individual gaming sessions in 6 weeks. A food knowledge questionnaire and a food frequency questionnaire were completed before and after game play. In addition, 39 of the participants’ parents answered a parent perception questionnaire after the game play. Results: Participants showed increased food knowledge from pregame (mean 56.9, SD 10.7) to postgame play (mean 67.8, SD 10.7; P<.001). In addition, there was a greater self-reported frequency in the consumption of cauliflower and broccoli (P<.001) and corn quesadillas (P<.001). They also indicated a lower self-reported intake of 10 unhealthy foods, including french fries (P=.003), candy and chocolate (P<.001), sweet soft cakes (P=.009), and soft drinks (P=.03). Moreover, most of the parents who answered the parent perception questionnaire agreed that their children showed greater interest in explaining why they should avoid some unhealthy foods (67%, 26/39), in distinguishing between healthy and unhealthy foods (64%, 25/39), and in the intake of fruits (64%, 25/39) and vegetables (59%, 23/39). Finally, 14 parents stated that they introduced some changes in their children's diet based on the comments and suggestions they received from their children. Conclusions: In an initial evaluation, children between 8 and 10 years old indicated an increased level in nutritional knowledge and their self-reported frequency intake of two healthy foods, and a decreased level in their self-reported intake of 10 unhealthy foods after playing FoodRateMaster. Moreover, the participants’ parents agreed that FoodRateMaster positively influenced their children’s attitudes toward several healthy eating behaviors. These results support that health games such as FoodRateMaster are viable tools to help young children increase their food knowledge and improve dietary behaviors. A follow-up randomized controlled trial will be conducted to assess the medium- and long-term effects of FoodRateMaster.

Background: The design and use of serious video games for children have increased in recent years. To maximize the effects of these games, it is essential to understand the children’s experiences through playing. Previous studies identified that enjoyment and user experience satisfaction of the players are principal factors that can influence the success of serious video games and the learning of their players. However, research about the relationship between enjoyment and user experience satisfaction with learning in children 8 to 10 years old is sparse. Objective: We examined the relationship of enjoyment and user experience satisfaction with the learning of children aged 8 to 10 years while playing a serious video game for health, FoodRateMaster. This serious video game teaches children about the characteristics of healthy and unhealthy foods and how to identify them in their environment. Methods: Children aged 8 to 10 years were recruited from a primary school in Mexico. Participants completed 12 individual gaming sessions with FoodRateMaster in 6 weeks. A food knowledge questionnaire was administered before and after game play to assess the players’ food knowledge. In addition, after the gaming sessions, the children’s enjoyment and user experience satisfaction were evaluated using the EGameFlow questionnaire and the Game User Experience Satisfaction Scale (GUESS) questionnaire. Results: We found significant positive associations for children’s (n=60) posttest knowledge with enjoyment (r58=0.36, P=.005) and user experience satisfaction (r58=0.27, P=.04). The children’s posttest knowledge scores were also positively correlated with challenge (r58=0.38, P=.003), knowledge improvement (r58=0.38, P=.003), and goal clarity (r58=0.29, P=.02) EGameFlow subscales and with narrative (r58=0.35, P=.006), creative freedom (r58=0.26, P=.04), and visual esthetics (r58=0.32, P=.01) GUESS subscales. Regression analysis indicated that the EGameFlow (F7,52=2.74, P=.02, R2=0.27) and the GUESS (F8,51=2.20, P=.04, R2=0.26) ratings significantly predicted the children’s posttest knowledge scores. EGameFlow challenge (β=0.40, t52=2.17, P=.04) and knowledge improvement (β=0.29, t52=2.06, P=.04) subscales significantly contributed to predicting children’s learning. None of the GUESS subscales significantly contributed to predicting children’s learning. Conclusions: The findings of this study suggest that both enjoyment and user experience satisfaction for children aged 8 to 10 years were positively correlated with their learning and that were significant predictors of it. Challenge, knowledge improvement, narrative, creative freedom, and visual esthetics subscales correlated positively with children’s learning. In addition, challenge and knowledge improvement contributed to predicting their learning. These results are relevant to consider during the design stages of serious games developed for young children’s learning purposes.

Background: Embodied conversational agents (ECAs) are advanced human-like interfaces that engage users in natural face-to-face conversations and interactions. These traits position ECAs as innovative tools for delivering interventions for promoting health-related behavior adoption. This includes motivational interviewing (MI), a therapeutic approach that combines brief interventions with motivational techniques to encourage the adoption of healthier behaviors. Objective: This study aims to identify the health issues addressed by ECAs delivering MI interventions, explore the key characteristics of these ECAs (eg, appearance, dialogue mechanism, emotional model), analyze the implementation of MI principles and techniques within ECAs, and examine the evaluation methods and primary outcomes of studies that use ECAs providing MI interventions. Methods: We conducted a scoping review following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) methodology. Our systematic search covered the PubMed, Scopus, IEEE Xplore, ACM Digital, and PsycINFO databases for papers published between January 2008 and December 2022. We included papers describing ECAs developed for delivering MI interventions targeting health-related behaviors and excluded articles that did not describe ECAs with human appearances and without the necessary evaluation or MI explanation. In a multistage process, 3 independent reviewers performed screening and data extraction, and the collected data were synthesized using a narrative approach. Results: The initial search identified 404 articles, of which 3.5% (n=14) were included in the review. ECAs primarily focused on reducing alcohol use (n=5, 36%), took on female representations (n=9, 64%), and gave limited consideration to user ethnicity (n=9, 64%). Most of them used rules-driven dialogue mechanisms (n=13, 93%), include emotional behavior to convey empathy (n=8, 57%) but without an automatic recognition of user emotions (n=12, 86%). Regarding MI implementation, of 14 studies, 3 (21%) covered all MI principles, 4 (29%) included all processes, and none covered all techniques. Most studies (8/14, 57%) conducted acceptability, usability, and user experience assessments, whereas a smaller proportion (4/14, 29%) used randomized controlled trials to evaluate behavior changes. Overall, the studies reported positive results regarding acceptability, usability, and user experience and showed promising outcomes in changes in attitudes, beliefs, motivation, and behavior. Conclusions: This study revealed significant advancements in the use of ECAs for delivering MI interventions aimed at promoting healthier behaviors over the past 15 years. However, this review emphasizes the need for a more in-depth exploration of ECA characteristics. In addition, there is a need for the enhanced integration of MI principles, processes, and techniques into ECAs. Although acceptability and usability have received considerable attention, there is a compelling argument for placing a stronger emphasis on assessing changes in attitudes, beliefs, motivation, and behavior. Consequently, inclusion of more randomized controlled trials is essential for comprehensive intervention evaluations.