Mobile Applications Research Articles

Developing and validating a content quality evaluation tool for cancer mobile applications

IntroductionCancer remains a leading cause of death, underscoring the importance of reliable health apps. This study aims to design and validate a content quality evaluation tool for cancer mobile applications ensuring they provide standard services and features to users.MethodThis descriptive-analytical study was conducted in steps including identifying mobile application contents, designing and validating evaluation tool. Studies published until February 19, 2023, in PubMed, Web of Science, Scopus, and Scientific Information Database (SID) databases, as well as mobile applications in Myket, Bazaar, and Google Play, were searched. Data extraction and expert panel validation followed, leading to four expert sessions to design the tool. Nineteen oncologists, health information management and medical informatics specialties were included in the study as a research community. The tool’s face validity, content validity and reliability were assessed using CVR (Content Validity Ratio), CVI (Content Validity Index), and Cronbach’s alpha respectively.ResultsOut of 242 initial search results in databases and 125 mobile applications in app stores, 25 articles and 20 mobile applications included based on the search limits. The designed tools consist of 8 main themes (prevention, diagnosis, treatment, follow-up, education, communication, requests/order and other) with 43 question items. All items received scores above 0.79 in CVI and 0.42 in CVR, leading to their approval and the reliability of the tool was accepted with a score of 0.967.ConclusionEvaluating cancer mobile applications can be very helpful in developing useful and supportive mobile apps, and assist health policymakers in identifying and recommending high-quality cancer-related apps. This will enable patients to choose the most suitable apps, enhancing the quality of cancer care.

Emerging Educational and Networking Platforms in Interventional Cardiology: Expanding Beyond Traditional Social Media.

With ubiquitous use of smartphones and other Internet-enabled devices, clinicians have access to a plethora of multimedia platforms that enable them to network, share educational content, and learn. Several platforms also enable resources that support clinical decision making. Traditional social media has been used extensively by cardiologists for several years and now extends to the entire care team including trainees, advanced practitioners, nurses and technologists. Numerous challenges include concerns about patient privacy and security, lack of peer-review and organization, and chances of propagating misinformation. More recently, newer networking platforms have emerged within cardiology to overcome several of the limitations of traditional social media. Additionally, mobile applications have emerged as an educational tool that provides clinicians with easy access to the latest information including guidelines, consensus documents, risk estimators and treatment algorithms. These point-of-care learning and decision-making tools are especially relevant and impactful in a procedural field like interventional cardiology. While professional societies are major drivers of these emerging platforms, there are also other stakeholders including educational institutions and the medical device industry. In this review article, we examine the role of traditional social media and describe newer networking platforms as well as mobile applications applicable to interventional cardiology with a discussion about their relative advantages and disadvantages.

Optimization and Performance Analysis of Real-time Speech Translation Systems Based on Mobile Technology

As globalization deepens and mobile technology rapidly advances, the demand for crosslinguistic communication has been steadily increasing, making real-time speech translation systems a research focus. However, given the limited computational capacity and storage space of mobile devices, optimizing system performance while maintaining translation quality has become a critical challenge. Current optimization approaches for real-time speech translation systems primarily focus on improvements to model architectures and hardware acceleration, often neglecting a systematic study of model compression. This is particularly evident when handling real-time data, where achieving both high efficiency and translation accuracy remains difficult. To address these challenges, a model compression method based on the connectionist temporal classification (CTC) criterion was proposed, along with an in-depth study of parameter compression tailored for mobile applications. The research focuses on two key areas: first, model compression techniques based on the CTC criterion were explored to enhance the efficiency of real-time speech translation; second, parameter compression methods were investigated to significantly reduce resource consumption in mobile applications while preserving translation quality. The aim of this study is to improve the performance and user experience of real-time speech translation systems on mobile devices.

The Integration of Thermal Energy Storage Within Metal Hydride Systems: A Comprehensive Review

Hydrogen storage technologies are key enablers for the development of low-emission, sustainable energy supply chains, primarily due to the versatility of hydrogen as a clean energy carrier. Hydrogen can be utilized in both stationary and mobile power applications, and as a low-environmental-impact energy source for various industrial sectors, provided it is produced from renewable resources. However, efficient hydrogen storage remains a significant technical challenge. Conventional storage methods, such as compressed and liquefied hydrogen, suffer from energy losses and limited gravimetric and volumetric energy densities, highlighting the need for innovative storage solutions. One promising approach is hydrogen storage in metal hydrides, which offers advantages such as high storage capacities and flexibility in the temperature and pressure conditions required for hydrogen uptake and release, depending on the chosen material. However, these systems necessitate the careful management of the heat generated and absorbed during hydrogen absorption and desorption processes. Thermal energy storage (TES) systems provide a means to enhance the energy efficiency and cost-effectiveness of metal hydride-based storage by effectively coupling thermal management with hydrogen storage processes. This review introduces metal hydride materials for hydrogen storage, focusing on their thermophysical, thermodynamic, and kinetic properties. Additionally, it explores TES materials, including sensible, latent, and thermochemical energy storage options, with emphasis on those that operate at temperatures compatible with widely studied hydride systems. A detailed analysis of notable metal hydride–TES coupled systems from the literature is provided. Finally, the review assesses potential future developments in the field, offering guidance for researchers and engineers in advancing innovative and efficient hydrogen energy systems.

Intention to Seek Mental Health Services During the 2022 Shanghai COVID-19 City-Wide Lockdown: Web-Based Cross-Sectional Study

Background The implementation of COVID-19 lockdown measures had immediate and delayed psychological effects. From March 27, 2022, to June 1, 2022, the Shanghai government enforced a city-wide lockdown that affected 25 million residents. During this period, mental health services were predominantly provided through digital platforms. However, limited knowledge exists regarding the general population’s intention to use mental health services during this time. Objective This study aimed to assess the intention of Shanghai residents to use mental health services during the 2022 Shanghai lockdown and identify factors associated with the intention to use mobile mental health services. Methods An online survey was distributed from April 29 to June 1, 2022, using a purposive sampling approach across 16 districts in Shanghai. Eligible participants were adults over 18 years of age who were physically present in Shanghai during the lockdown. Multivariable logistic regression was used to estimate the associations between demographic factors, lockdown-related stressors and experiences, physical and mental health status, and study outcomes–mobile mental health service use intention (mobile applications and WeChat Mini Programs [Tencent Holdings Limited]). Results The analytical sample comprised 3230 respondents, among whom 29.7% (weighted percentage; n=1030) screened positive for depression or anxiety based on the 9-item Patient Health Questionnaire or the 7-item Generalized Anxiety Disorder Scale. Less than one-fourth of the respondents (24.4%, n=914) expressed an intention to use any form of mental health services, with mobile mental health service being the most considered option (19.3%, n=728). Only 10.9% (n=440) used digital mental health services during the lockdown. Factors associated with increased odds of mobile mental health service use intention included being female, being employed, being a permanent resident, experiencing COVID-19–related stressors (such as loss of income, food insecurity, and potentially traumatic experiences), and having social and financial support. Individuals with moderate or severe anxiety, as well as those with comorbid anxiety and depression, demonstrated a higher intention to use mobile mental health services. However, individuals with depression alone did not exhibit a significantly higher intention compared with those without common mental disorders. Conclusions Despite a high prevalence of common mental disorders among Shanghai residents, less than one-fourth of the study respondents expressed an intention to use any form of mental health services during the lockdown. Mobile apps or WeChat Mini Programs were the most considered mental health service formats. The study provided insights for developing more person-centered mobile mental health services to meet the diverse needs of different populations.

Validity of the PEDOMETER® mobile application for evaluating cardiorespiratory fitness and fatigue resistance in cardiac rehabilitation. A pilot study

A mobile application (app) may facilitate the administration of the 6-minute walk test (6MWT), a commonly used tool in cardiac rehabilitation. This pilot study, conducted with cardiac rehabilitation patients, aimed to evaluate: 1) the criterion validity of the PEDOMETER® app compared to the 6MWT in assessing cardiorespiratory fitness and fatigue resistance, and 2) the construct validity of the PEDOMETER ® app in relation to the Hospital Anxiety and Depression Scale (HADS) and the Health Index of the EuroQol-5D 3L questionnaire (EQ-5D). Twenty patients undergoing cardiac rehabilitation at HM Puerta del Sur Hospital (Mostoles, Spain) performed the 6MWT while using the PEDOMETER ® app on a smartphone worn on their arm. Pearson's correlation coefficient was calculated to assess the relationship between the distance recorded in meters during the 6MWT and that recorded by the app, as well as between the app and the anxiety and depression dimensions of the HADS, and the Health Index EQ-5D. The criterion validity of the 6MWT and the PEDOMETER ® app was excellent (r = 0.981; p < 0.001). Regarding construct validity, the app showed a moderate negative correlation with the depression dimension of the HADS (r= -0.493), but not with the anxiety dimension or the EQ-5D. The free PEDOMETER ® app demonstrates excellent criterion validity when compared to the 6MWT. The app shows a moderate negative correlation with the depression dimension of the HADS.

ArtStory Beats: Highlighting interactions between visual arts and music with storytelling

In this article, we present the use-case of ArtStory Beats, a mobile application that aims to reflect the creative dialogue between visual arts and music, offering stories about the vibrant interactions that take place within the artistic communities over time. The app use is quite simple: it scans physical copies of visual artworks and provides audio-enhanced stories. However, the audio is not intended to be background or supplementary informative material. It is a music song that somehow relates to the visual artwork, and the stories tell you how. First, we describe our content creation process along with the design of the mobile application. Then we present the results from a qualitative user study with 15 participants, investigating the role and effects of music over several dimensions: overall experience, user satisfaction, and most notably, cultural engagement. The outcomes of the study reveal the potential of the approach to foster curiosity, capture the user's attention and support a deeper, reflective connection of the user with the combined artworks. Finally, we discuss the potential use of ArtStory Beats in different contexts, as a remote but also onsite experience, elaborating on related challenges and future directions.

Deep Learning-Based Control System Design for Emergency Vehicles through Intersections

This paper addresses the challenge of integrating priority passage for emergency vehicles with optimal intersection control in modern urban traffic. It proposes an innovative strategy based on deep learning to enable emergency vehicles to pass through intersections efficiently and safely. The research aims to develop a deep learning model that utilizes intersection violation monitoring cameras to identify emergency vehicles in real time. This system adjusts traffic signals to ensure the rapid passage of emergency vehicles while simultaneously optimizing the overall efficiency of the traffic system. In this study, OpenCV is used in combination with Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to jointly complete complex image processing and analysis tasks, to realize the purpose of fast travel of emergency vehicles. At the end of this study, the principle of the You Only Look Once (YOLO) algorithm can be used to design a website and a mobile phone application (app) to enable private vehicles with emergency needs to realize emergency passage through the application, which is also of great significance to improve the overall level of urban traffic management, reduce traffic congestion and promote the development of related technologies.

Hubungan Pengetahuan dengan Penggunaan Mobile JKN : Studi Kasus RS Santa Elisabeth Medan

Mobile JKN is a form of digital transformation in the form of an application that can be used by JKN participants anywhere and at any time and is expected to reduce queues when registering patients. At the Santa Elisabeth Hospital in Medan, there are still patients who do not understand how to use the JKN mobile application because the level of knowledge about Mobile JKN is still relatively low, especially in patients who have low education and are entering old age so that the available health services are not utilized optimally. The aim of this research is to identify the relationship between knowledge and the use of mobile JKN at Santa Elisabeth Hospital Medan in 2024. The analytical research design uses a cross sectional approach. The sample for this research was 95 respondents with the sampling technique being accidental sampling. The instrument used was a questionnaire. Data analysis using the Chi-square test. The results of this study showed that 80 patients' knowledge was in the good category (84.2%), 51 people's use of mobile JKN was in the good category (53.7%). The results of the Chi-square test obtained a p-value of 0.002 (p&lt;0.05) indicating that there is a significant relationship between knowledge and the use of mobile JKN at Santa Elisabeth Hospital Medan in 2024.

LEARNING QIRA'AH AL-QUR'AN USING THE TILAWATI MOBILE APPLICATION IN THE DIGITAL ERA

The aim of this research is to describe learning Qira'ah Al-Qusr'an using the Tilawati Mobile application in the digital era. This application is designed to facilitate interactive learning of recitation of Al-Qur'an by using modern features such as voice recording, real-time evaluation, qori and mutual options, and visualization of recitation. The main aim of this application is to improve and assist the users’ Al-Qur'an reading skills with greater accuracy and fluency while increasing understanding of the rules of recitation and the context of the meaning contained in the Quran. This research employed library research as it is applicable with the aim of this research. The research procedures covered documents the implementation of technology in Islamic religious education, explores user responses to this digital innovation, and elaborates its impact on the understanding and development of qira'ah competencies. By integrating technology into religious learning, the Tilawati Mobile application acts as an effective tool in facilitating holistic learning of Al-Qur’an in the digital era.

Security analysis and evaluation of mobile banking applications in Nigeria

Rapid fintech adoption across the world is so ubiquitous. To facilitate more adoption in Nigeria, recently the Central Bank of Nigeria (CBN) introduced several policies that support cashless banking. Nowadays, Nigerian banks users could perform most of their daily transactions from any desired location using mobile banking applications. In the literature, there are insufficient studies that comprehensively evaluate the security strength or risks of these applications. Generally, insecure mobile banking applications could lead to financial fraud, violations of privacy, identity theft and eroded user confidence. Considering the situation, there is need to conduct research which comprehensively assess security of the applications. Consequently, in this paper we analyzed and evaluated the security of identified popular mobile banking applications in Nigeria. We conducted the analysis work using automated and manual static analysis methods. Then, we evaluated the security of the applications using multi-criteria decision-making technique. Our results revealed that most of the applications have several security challenges in form of vulnerabilities and insecure coding practices. Hence, our findings have shown the applications need further improvements for better security and safety.

Diabetes Education Program for Nursing Students: A Systematic Review and Meta-Analysis.

The purpose of this study was to summarise the current state of the science on diabetes mellitus education programs for nursing students. A systematic review and meta-analysis. Eligible studies were identified by searching PubMed, EMBASE, CINAHL, and Cochrane Library databases. Randomised controlled trials and quasi-experimental studies, published in English between 2013 and 2022, that examined diabetes education programs for nursing students were considered in the review. The quality of the articles was evaluated using the Joanna Briggs Institute's Critical Appraisal Checklist. Key information such as authors, study focus, population, sample size, details of intervention and control group treatments, outcome variables, and main findings were extracted and summarised in a data extraction form for further analyses and syntheses. The literature search identified 464 articles, from which 13 studies were evaluated in the systematic review. Most studies (n = 12, 92.3%) used technology-based teaching methods, such as high-fidelity simulations, mobile applications, and virtual reality simulations. Regarding the evaluation of diabetes education program effectiveness, the majority of studies showed significant improvements in knowledge (n = 8, 61.5%), followed by satisfaction with learning (n = 4, 30.8%), nursing skill performance (n = 3, 23.1%), and self-confidence (n = 3, 23.1%) in nursing students. In meta-analyses, technology-based teaching interventions, compared to traditional education, showed no statistically significant improvement in diabetes knowledge (standard mean difference 9.52, 95% CI [-0.18, 19.21], p = 0.05) and self-efficacy (standard mean difference 24.09, 95% CI [-10.75, 58.92], p = 0.18). Despite this, technology-based methods demonstrated favourable effects on knowledge and self-efficacy against traditional education. Findings highlight the importance of emerging technology-based diabetes education programs tailored for nursing students, crucial for enhancing positive educational outcomes. No Patient or Public Contribution.

Exploring the Relationship Between eHealth Literacy and Diabetes Knowledge, Self-Efficacy, and Self-Care Behaviors in Chinese Diabetic Patients: A Cross-Sectional Study.

The importance of online educational resources enhancing self-management among patients is underscored by the growing prevalence of diabetes. It is crucial to comprehend how patients with diabetes in China seek diabetes-related information and use mobile applications (apps) designed for diabetes management. Although the Knowledge-Attitude-Practice theory plays an integral role in diabetes management, in-depth studies on eHealth literacy (eHL) and Knowledge-Attitude-Practice in populations with diabetes remain scarce. This study was developed to provide insights into the online information-seeking behaviors of and diabetes apps used by individuals living with diabetes by exploring the relationships among eHL, diabetes knowledge, self-efficacy, and self-care behaviors. A cross-sectional survey was conducted from November 2022 to June 2023 on 380 inpatients with diabetes at the Yantai Yuhuangding Hospital in Shandong Province. The participants voluntarily completed surveys covering sociodemographic characteristics, diabetes status, diabetes app usage, online diabetes information searching, eHL, diabetes knowledge, self-efficacy, and self-care behaviors. Structural equation modeling analyses were employed to assess model fitness and the interrelationships between latent constructs and observable variables. Of the 380 participants, 57.1% (217/380) reported actively seeking diabetes information online, whereas only 3.7% (14/380) had used diabetes apps. eHL was shown to have a direct effect on diabetes knowledge (β = 0.377, p < .001) but no direct impact on self-care behaviors (β = 0.017, p = .860). However, an indirect effect on self-care behaviors was observed via diabetes knowledge and self-efficacy. The results of this study indicate that, despite widespread online information-seeking behavior, diabetes app utilization remains limited in China. Also, the findings indicate enhancing patients' eHL contributes to more comprehensive diabetes knowledge. Furthermore, eHL was shown to influence self-care behaviors via diabetes knowledge and self-efficacy. A self-managed intervention strategy should be developed to improve eHL that utilizes internet resources to improve patients' knowledge and self-efficacy and promote better self-care behaviors.

Development of an eHealth Intervention Prototype to Prevent Health Risk Behaviors Among Hispanic Adolescents: A User-Centered Formative Study

Health risk behaviors continue to disproportionately affect Hispanic youth. Despite the existence of successful family and school-based interventions, there is a need for developing and testing individually-based preventive interventions that are easily accessed and widely disseminated. Therefore, this study aimed to develop a prototype (proof of concept) for an individual-level mobile application (app), informed by Hispanic parents and adolescents, to prevent/reduce drug use and sexual risk behaviors among Hispanic youth. An iterative user-centered approach was used to inform the development of the app prototype via focus groups with 66 participants (n = 46 adolescents, n = 20 parents). A coding team analyzed data from the focus groups and identified major themes. The coding team summarized interview data into sub-categories that yielded five intervention modules for Hispanic adolescents, three more than originally proposed (i.e., drug use and sexual risk behaviors): (1) effective communication, (2) depression, (3) sexual health, (4) drug use, and (5) mindfulness. A mobile application for health risk behaviors can be used as an additional preventive tool to decrease the existing behavioral health disparities among Hispanic youth. Incorporating a user-centered approach to inform development is important for including the needs and voices of this population.

The Effect of Sleep Health Education Based on Mobile Application on Sleep Quality of the Elderly: A Quasi-Experimental Study

The Effect of Sleep Health Education Based on Mobile Application on Sleep Quality of the Elderly: A Quasi-Experimental Study

GAFAR revisited—Exploring the limits of point cloud registration on sparse subsets

Robust estimation of a rigid transformation aligning two point clouds is a fundamental building block in many tasks in computer vision and robotics, such as mapping, (re-)localization, SLAM or 3D scanning, and many more. Deep-Learning based registration methods have proven superior in terms of robustness to outliers and bad initializations, yet their resource needs often render them unsuitable for applications where space, energy and real-time constraints come into play. Since - provided a good initialization - conventional registration algorithms like ICP prove to be fast and accurate, we may relax the requirements of absolute precision of a learning-based matcher and instead focus our attention on efficiency, robustness and generalization.In prior work, we introduced our small, fast and light-weight registration algorithm GAFAR, which works on sparse subsets of point clouds and exhibits a small enough footprint to be deployed on edge devices in mobile applications, while still providing accurate results and promising generalization ability. Based thereon, we develop this idea further towards applicability on real world data with improvements to the algorithm as well as further evaluations and analyses. We showcase this by applying it to Kitti Odometry Benchmark and 3DMatch dataset as well as demonstrating its usability on edge devices. The code and trained weights are published in https://github.com/mordecaimalignatius/GAFAR/.

Enhancing Driving Safety and Environmental Consciousness through Automated Road Sign Recognition Using Convolutional Neural Networks

Traffic accidents remain a pressing public safety concern, with a substantial number of incidents resulting from drivers' lack of attentiveness to road signs. Automated road sign recognition has emerged as a promising technology for enhancing driving assistance systems. This study explores the application of Convolutional Neural Networks (CNNs) in automatically recognizing road signs. CNNs, as deep learning algorithms, possess the ability to process and classify visual data, making them well-suited for image-based tasks such as road sign recognition. The research focuses on the data collection process for training the CNN, incorporating a diverse dataset of road sign images to improve recognition accuracy across various scenarios. A mobile application was developed as the user interface, with the output of the system displayed on the app. The results show that the system is capable of recognizing signs in real time, with average accuracy for sign recognition from a distance of 10 meters: i) daytime = 89.8%, ii) nighttime = 75.6%, and iii) rainy conditions = 76.4%. In conclusion, the integration of CNNs in automated road sign recognition, as demonstrated in this study, presents a promising avenue for enhancing driving safety by addressing drivers' attentiveness to road signs in real-time scenarios.

Leveraging Fog Computing for Security‐Aware Resource Allocation in Narrowband Internet of Things

ABSTRACTNarrowband Internet of Things (NB‐IoT) is LPWAN operating using narrowband spectrum in IoTs, requiring low data rates and long battery life. Since NB‐IoT does not support handover, the requirement to sustain network connectivity during mobility may result in fake base station connections, hence applications are limited to stationary use‐cases. Researchers propose extending NB‐IoT in mobile applications, owing to higher signal quality and battery life, despite DoS attacks due to low bandwidth. As NB‐IoT is resource‐constrained, resources must be allocated based on application with data processing offloaded to optimise performance. Cloud servers, being centralised and memory‐intensive, may result in increased computational delay, lower throughput, and DoS attacks in NB‐IoT. Hence, in this paper, we implement fog computing, a decentralised technology, providing scalability, reduced bandwidth, and enhanced privacy, to provide distributed processing. Additionally, we develop secure handover protocols for private and service‐provider‐controlled fog networks under normal and cell‐splitting conditions to minimise fake base station attacks and provide seamless handover. We introduce reputation‐based mechanisms to determine device integrity and differentiate faulty behaviour and attacks. Further, we implement real‐time application‐aware resource allocation and QoS‐based load‐balancing using deep learning to distribute data processing between devices, fog and cloud servers. We simulate and prototype protocols on iFogSim2 and Raspberry Pi 4. Security of the fog computing framework is validated against various attacks and formally verified using Scyther. Evaluation shows that our approach consumes 12% and 43.75% lower power and communication overhead and approximately 6 and 16 times lower execution time and memory compared with existing solutions, thus making our approach lightweight.

The integrated smart system to assist elderly at home

Some elders prefer to live independently rather than in a nursing home. However, they need to be assisted because of health and safety reasons. We developed an integrated system consisting of a smart home, a mobile robot, and a wearable device, using message queuing telemetry transport (MQTT) protocol to communicate with each other. The smart home can be monitored and controlled via a mobile application. We use the telepresence robot equipped with a light detection and ranging (LIDAR) sensor to perform simultaneous localization and mapping (SLAM) and autonomous navigation. The wearable device application is used to detect older adults' heart rate, the steps taken, and the burnt calories. We also add pose detection and location estimation using a depth camera powered by an efficient deep-learning algorithm. We also developed an Android application as a dashboard that monitors and controls all system components. Our system can facilitate communication between its various components.

The impact of social media and gamification of a mobile vocabulary learning app: Self-regulation and learning persistence

Scholars indicated that learners who are strategic with their language learning (e.g., self-regulated learning (SRL), cognitive and meta-cognitive strategies) tend to be more efficient, resourceful, and flexible, and thus have better language learning outcomes (Oxford, 2016; Heo et al., 2012; Plonsky, 2011). Besides focusing on knowledge, language learners, as L2 teachers already do, need to prepare themselves with self-regulated learning skills, so they can achieve the target language learning outcomes. This study selected Shanbay Danci (SBDC), a popular mobile vocabulary application (Wang, 2020, Su &amp; Su, 2015), as the intervention. SBDC allows learners to share not only information about their goals but also badges (a gamification feature) demonstrating their learning outcomes. To explore the impacts of the badge-sharing feature on learners’ perceived self-regulation level, vocabulary learning persistence, and self-regulated learning experiences, a total of 77 undergraduates, who were enrolled in College English courses at a Chinese institution, participated in the study. Results showed participants in the badge-sharing group (experimental) tended to have significantly higher SRL levels (e.g., better management of time and the learning environment, being more strategic in completing tasks, selecting an appropriate learning environment, setting learning goals, and self-evaluating their learning progress) than those in the control group who were not enrolled in any badge sharing group. Moreover, those in the badge-sharing group demonstrated higher persistence in learning vocabulary, with a completion rate of 70%, versus 56% in the control group. Qualitative results indicated that badge sharing enhanced learners’ self and shared regulation capacity in the process of learning vocabulary.