The ability to interpret emotions from facial expressions is essential in social interaction. However, these abilities are complex for individuals with autism spectrum disorder (ASD). This study aims ...to develop a real-time human facial expression recognition system on a mobile phone application to help individuals with ASD. The expression recognition model was developed using the FER-2013 dataset with the VGG-16 architecture. The experimental results show that the model obtained from the VGG-16 architecture can produce the best accuracy and average F1 scores with 0.91 and 0.91, respectively. We also experiment with three individuals with ASD, resulting in increasing interest in learning the expression.
The number of mobile health apps (MHAs), which are developed to promote healthy behaviors, prevent disease onset, manage and cure diseases, or assist with rehabilitation measures, has exploded. App ...store star ratings and descriptions usually provide insufficient or even false information about app quality, although they are popular among end users. A rigorous systematic approach to establish and evaluate the quality of MHAs is urgently needed. The Mobile App Rating Scale (MARS) is an assessment tool that facilitates the objective and systematic evaluation of the quality of MHAs. However, a German MARS is currently not available.
The aim of this study was to translate and validate a German version of the MARS (MARS-G).
The original 19-item MARS was forward and backward translated twice, and the MARS-G was created. App description items were extended, and 104 MHAs were rated twice by eight independent bilingual researchers, using the MARS-G and MARS. The internal consistency, validity, and reliability of both scales were assessed. Mokken scale analysis was used to investigate the scalability of the overall scores.
The retranslated scale showed excellent alignment with the original MARS. Additionally, the properties of the MARS-G were comparable to those of the original MARS. The internal consistency was good for all subscales (ie, omega ranged from 0.72 to 0.91). The correlation coefficients (r) between the dimensions of the MARS-G and MARS ranged from 0.93 to 0.98. The scalability of the MARS (H=0.50) and MARS-G (H=0.48) were good.
The MARS-G is a reliable and valid tool for experts and stakeholders to assess the quality of health apps in German-speaking populations. The overall score is a reliable quality indicator. However, further studies are needed to assess the factorial structure of the MARS and MARS-G.
Chronic pain is a major health issue requiring an approach that not only considers medication, but also many other factors included in the biopsychosocial model of pain. New technologies, such as ...mobile apps, are tools to address these factors, although in many cases they lack proven quality or are not based on scientific evidence, so it is necessary to review and measure their quality.
The aim is to evaluate and measure the quality of mobile apps for the management of pain using the Mobile App Rating Scale (MARS).
This study included 18 pain-related mobile apps from the App Store and Play Store. The MARS was administered to measure their quality. We list the scores (of each section and the final score) of every app and we report the mean score (and standard deviation) for an overall vision of the quality of the pain-related apps. We compare the section scores between the groups defined according to the tertiles via analysis of variance (ANOVA) or Kruskal-Wallis test, depending on the normality of the distribution (Shapiro-Wilk test).
The global quality ranged from 1.74 (worst app) to 4.35 (best app). Overall, the 18 apps obtained a mean score of 3.17 (SD 0.75). The best-rated sections were functionality (mean 3.92, SD 0.72), esthetics (mean 3.29, SD 1.05), and engagement (mean 2.87, SD 1.14), whereas the worst rated were app specific (mean 2.48, SD 1.00), information (mean 2.52, SD 0.82), and app subjective quality (mean 2.68, SD 1.22). The main differences between tertiles were found on app subjective quality, engagement, esthetics, and app specific.
Current pain-related apps are of a certain quality mainly regarding their technical aspects, although they fail to offer information and have an impact on the user. Most apps are not based on scientific evidence, have not been rigorously tested, and the confidentiality of the information collected is not guaranteed. Future apps would need to improve these aspects and exploit the capabilities of current devices.
There is a lack of theory-driven empirical research that evaluates outcomes of location-based augmented reality (AR) applications with the purpose of improving instructional design and use ...guidelines. The primary aim of this study was to compare the effectiveness of two historical reasoning guide protocols, one based on prior research by Harley and colleagues (2016a; the other an extension) while learners used a mobile AR app to learn about history. Learners reported significantly higher levels of enjoyment and curiosity from learning about history than using the app itself, though mean levels were high for both—in contrast to negative emotions. Results suggest that the new and extended historical reasoning guide protocol succeeded in fostering higher levels of knowledge than the former. Findings also revealed that learners reported significantly higher levels of task value after the guided tour compared to their pre-guided-tour responses. Implications and future directions are discussed.
•Significantly higher levels of enjoyment and curiosity from learning about history than from interacting with the app.•Learning outcomes significantly higher in the extended protocol than the previously developed one (high scores in both).•Significantly higher levels of task value reported after the guided tour compared to learners’ pre-guided-tour responses.•Significantly higher levels of enjoyment from learning about the Arts Building than history learning in formal settings.•Findings paint a coherent and optimistic picture regarding the use of mobile AR apps for teaching history.
One of the most applied value scales in research is personal shopping value (PSV) by Babin, Darden, and Griffin (1994). PSV assesses consumers’ shopping experiences along hedonic and utilitarian ...value. The purpose of this research is the corroboration of the original article and the PSV scale to investigate the impact of the past 25 years on the scale’s dimensionality and item composition. The corroboration mirrors the original store environment, while an extension additionally considers two contemporary shopping environments: online websites and mobile apps. Results across six studies confirm shopping value’s two-dimensional structure of work and fun. However, individual items capturing hedonic and utilitarian value deviate from original PSV scale items in number and nature for current stores, online, and mobile apps environments. Researchers and practitioners should exhibit caution to blindly administer or adapt measures without considering temporal or contextual aspects of the scale that limit its applicability.
Context: Source code reuse has been widely accepted as a fundamental activity in software development. Recent studies showed that StackOverflow has emerged as one of the most popular resources for ...code reuse. Therefore, a plethora of work proposed ways to optimally ask questions, search for answers and find relevant code on StackOverflow. However, little work studies the impact of code reuse from StackOverflow.
Objective: To better understand the impact of code reuse from StackOverflow, we perform an exploratory study focusing on code reuse from StackOverflow in the context of mobile apps. Specifically, we investigate how much, why, when, and who reuses code. Moreover, to understand the potential implications of code reuse, we examine the percentage of bugs in files that reuse StackOverflow code.
Method: We perform our study on 22 open source Android apps. For each project, we mine their source code and use clone detection techniques to identify code that is reused from StackOverflow. We then apply different quantitative and qualitative methods to answer our research questions.
Results: Our findings indicate that 1) the amount of reused StackOverflow code varies for different mobile apps, 2) feature additions and enhancements in apps are the main reasons for code reuse from StackOverflow, 3) mid-age and older apps reuse StackOverflow code mostly later on in their project lifetime and 4) that in smaller teams/apps, more experienced developers reuse code, whereas in larger teams/apps, the less experienced developers reuse code the most. Additionally, we found that the percentage of bugs is higher in files after reusing code from StackOverflow.
Conclusion: Our results provide insights on the potential impact of code reuse from StackOverflow on mobile apps. Furthermore, these results can benefit the research community in developing new techniques and tools to facilitate and improve code reuse from StackOverflow.
This study investigates how technology acceptance model (TAM) factors and social factors determine customer purchase intention. Although previous studies on mobile apps have investigated TAM, ...critical social factors have been neglected, thus, reinforcing the need to study the latter’s contribution to consumer purchase intention. Accordingly, this study examines social influence and peer influence in the TAM and collects 777 questionnaires from Digikala app users. Data were then analysed using structural equation modelling by AMOS. The findings indicate that perceived usefulness does not have a significant effect on attitude towards mobile app use. However, perceived ease of use, social and peer influence, and intention to purchase were shown to exhibit positive effects on attitude in this regard. The results also demonstrate that attitude towards mobile app use is a full mediator on three paths of the model. Finally, moderation analysis showed that only age has a mediating effect on the path from perceived usefulness to attitude towards mobile app use.
Behavioral theory is an important factor for designing digital health tools for diabetes to increase adherence to treatment. Many digital health products have not incorporated this method for ...achieving behavior change. This oversight might explain the disappointing outcomes of many products in this class. Four theories reported to be capable of enhancing the performance of digital health tools for diabetes include (1) Integrate, Design, Assess, and Share (IDEAS); (2) the Behaviour Change Wheel; (3) the Information-Motivation-Behavioral skills (IMB) model; and (4) gamification. Well-designed digital health tools are most likely to be effective if they are deployed in a patient-centered care setting established upon principles of sound behavioral theory. Behavioral theory can increase the effectiveness of digital tools and promote a receptive environment for their use.
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