User Experience Research Articles

Exploring the elderly user experience in using digital health interventions: a case study of the MySejahtera application

In recent years, there has been increased interest in using digital health tools to improve healthcare outcomes and encourage healthy lifestyles. However, older adults, who often face challenges with technology, may encounter difficulties when using these tools. This study focused on understanding how elderly individuals experience the MySejahtera app, a digital health tool designed to help manage health during a pandemic. The research involved 30 elderly users through focus groups, interviews, and user experience evaluations. The study identified key themes in the elderly user experience, such as the need for simplicity, efficiency in the app's interface, ease of learning, concerns about security, and emotional reactions. Recommendations were made to enhance the engagement of elderly users with digital health apps. While the MySejahtera app shows promise for older adults, the study highlights the importance of addressing specific design considerations and providing support to improve user satisfaction. Overall, the research offers valuable insights and recommendations for designing and implementing digital health applications that better meet the needs and preferences of elderly users.

Internet behavioral models for improving internet quality of service or user profiling: a systematic literature review

Internet behavior models have found applications across diverse domains, notably in internet addiction, customer satisfaction analysis, user purchasing behavior prediction, and optimizing internet of things (IoT) sensor performance. However, a notable gap exists in exploring these models in enhancing internet quality of service (QoS), specifically in campus settings, intricately linked to the nuances of students' online behavior. This study elucidates the strategic utilization of internet behavioral models for augmenting internet QoS and facilitating user behavior analysis. Creating datasets grounded in internet users' access behavior represents a pivotal phase, with explicit, implicit, and mixed methods emerging as the prevailing approaches. In this comprehensive literature review, we systematically scrutinized the methods, techniques, and inherent characteristics of constructing internet behavior models according to a systematic literature review process. The qualitative findings extracted from the systematic review encapsulated 1,046 articles, meticulously classified according to predefined inclusion and exclusion criteria. Subsequently, 35 articles were judiciously selected for in-depth analysis. This study culminated in identifying the most pertinent methodologies and salient features pivotal to construct robust internet behavior model for improving internet QoS and user experience.

Improving 4G LTE network quality using the automatic cell planning

The growing demand for network services leads to an increase in traffic load on eNodeB, resulting in decreased network quality and performance, necessitating optimization. This research analyses the results of optimising 4G reference signal received power (RSRP), signal to interference noise ratio (SINR) and throughput parameters using the automatic cell planning (ACP) method. ACP has been shown to significantly improve the performance and quality of 4G LTE networks compared to traditional cell planning methods. Based on the standard parameter RSRP, increased after ACP optimisation which is dominant in the range ≥ -100 s.d ˃ -85 dBm and obtained an average value of -98.59 dBm with good category. The average SINR has increased by 18.23 dB with a good category. The dominant throughput is in the 14,000 Kbps range with an average value of 50,241.08 Kbps with the excellent category. The ACP method can enhance the performance of 4G LTE networks, potentially addressing operator issues of unstable network quality due to poor coverage. The ACP method significantly enhances 4G LTE network performance, coverage, and user experience, potentially addressing unstable network quality due to poor coverage. This research is crucial for both users and the telecoms industry.

The green Fintech paradox: understanding the dynamics of green brand positioning and user decision-making in the digital financial landscape

Purpose The ever-evolving landscape of financial technology (Fintech) has revolutionised payment methods and raised questions about what drives user behaviour in adopting these innovative solutions. This study, using narrative transportation theory as an underpinning theory, aims to investigate into the dynamics of green user behaviour in adopting Fintech payments. Design/methodology/approach This study used a deductive approach, and with data obtained from 635 respondents through the purposive sampling technique, partial least squares structural equation modelling was employed to yield significant insights. Findings The study found a positive association between green brand positioning and product differentiation. However, it unexpectedly didn't impact user attitudes towards Fintech payments. Green brand image and perceived performance positively influenced product differentiation. Perceived product differentiation fully mediated the association between green brand positioning and user attitudes. The study introduced fear of missing out's (FOMO) moderating role, enriching eco-conscious marketing insights and user behaviour understanding. Research limitations/implications This study reveals crucial implications for marketers, policymakers and user experience (UX) designers operating within the Fintech industry. It emphasises green brand positioning's impact on product differentiation, user attitudes and its mediating role. It advocates for sustainability integration, innovation, strategic messaging and user-centric improvements to optimise user perceptions and competitiveness in the evolving Fintech landscape. The study's cross-sectional design may limit the ability to establish causal relationships over time and overlook temporal changes in green Fintech adoption dynamics; thus, longitudinal studies are warranted to better understand the evolving nature of user attitudes and behaviours towards green Fintech payments. Originality/value This study adds novelty to the existing body of literature by introducing the dimension of innovation appeal to green brand positioning and employing narrative transportation theory in the Fintech realm. The findings also add novelty by highlighting the moderating impact of fear of missing out in predicting the association between green brand positioning and product differentiation in the realm of green Fintech and green use behaviour.

User Experience of a Just-in-Time Smartphone Resonance Breathing Application for Substance Use Disorder: Acceptability, Appropriateness, and Feasibility.

Addressing the negative impact of substance use disorders (SUDs) on individuals, families, and communities is a public health priority. Most treatments and interventions require engagement with a healthcare provider or someone who can offer recovery support. The need for interventions that facilitate self-management of relapse triggers at the moment they occur is also critical. Our study aimed to explore the user experience of individuals using a just-in-time smartphone episodic resonance breathing (eRPB) intervention to address stress, anxiety, and drug cravings. We conducted an 8-week pilot study of the eRPB with 30 individuals in recovery from SUD. Data on 3 indicators of user experience-acceptability, appropriateness, and feasibility-were collected using survey questions (n = 30) and semi-structured interviews (n = 11). We performed univariate analysis on the survey data and deductive thematic analysis on the qualitative data. A majority of the survey respondents agreed that the application (app) was acceptable (> 77%), appropriate (> 82%), and feasible (> 89%). Several interview participants stated that the app helped them relax and manage stress and cravings and expressed appreciation for the simplicity of its design. Participants also reported barriers to feasibility (such as forgetting to use the app) and recommendations for improvement (such as the addition of motivational messages). Our findings show that individuals in recovery from SUD had highly positive experiences with the eRPB app. A positive user experience may improve adherence to the intervention and, ultimately, the self-management of stress, anxiety, and craving relapse triggers.

Fog computing in classrooms: boosting efficiency, responsiveness, user experience

In the context of rapidly advancing smart education systems, the effective management and optimization of modern classroom remain critical challenges. This research presents a novel methodology leveraging cloud and fog computing-based simulations, with a specific focus on the implementation of iFogSim. Empirical findings validate the efficacy of fog computing in monitoring classrooms, demonstrating significant improvements in performance metrics compared to traditional cloud computing architectures. Specifically, fog computing ensures remarkably low latency, with a mere 7 milliseconds, even with scalable integration across multiple classrooms. In contrast, cloud computing infrastructures exhibit considerably higher initial latencies, starting at 210 milliseconds, which further escalate with the increasing number of monitored classrooms. Furthermore, our analysis reveals substantially lower network overhead associated with fog computing, measuring at 5,231.8 kilobytes, in sharp contrast to the significantly higher network usage of 80,808 kilobytes observed with cloud computing solutions. These findings underscore the potential of fog computing as a promising solution for efficient and real-time management classroom in smart education environments.

Human-centered design for flexible and inclusive housing

ABSTRACT Studies on collective housing rarely adopt a human-centered perspective. This study introduces a model for designing and analyzing residential buildings based on the user experience (UX) of apartments. The analysis focuses on two areas: a morphological review of the conditions of flexibility and adaptability of fundamental areas of households and a UX analysis with a functional evaluation of basic daily uses of the apartments, displayed through a graphic assessment during the project definition stage. Applied to residential projects in Barcelona, this analysis shows endemic defects in housing design, such as dimensional deficiencies, a lack of spatial versatility, and unforeseen functionalities, which pose a risk to gender hierarchies at home.

U-Shape Relationship of Design Innovation and User Experience

U-Shape Relationship of Design Innovation and User Experience

U-Shape Relationship of Design Innovation and User Experience

U-Shape Relationship of Design Innovation and User Experience

Technology Acceptance Analysis Using UTAUT: A Study of QRIS Acceptance during the Pandemic

Background: The COVID-19 pandemic situation has compelled society to practice physical distancing. One of the government's efforts is to encourage the use of the QRIS payment method to minimize direct physical contact during transactions. Objective: The purpose of this research is to analyze the primary driving factors in the adoption of QRIS technology. The research urgency is to determine the most contributing predictor from the variables within the UTAUT model among the people of Jabodetabek. Methods: This research used the quantitative method by conducting an online survey among 384 respondents distributed across the Jabodetabek region. The sampling technique utilized was non-purposive sampling with criteria including domicile, age, reasons, frequency, and experience of QRIS usage. Conclusion: The results of the factor analysis test indicate that the performance expectancy and effort expectancy variables are combined into one variable, while the social influence variable is divided into two independent variables. The research findings reveal that the perceived risk variable is the predictor with the most significant contribution in the context of the pandemic situation. Future researchers are expected to be able to develop the research model in other contexts with different goals.

User Feedback and Experiences of a Novel Decision Support Tool to Improve Rates of Heart Failure Therapy

User Feedback and Experiences of a Novel Decision Support Tool to Improve Rates of Heart Failure Therapy

Understanding the User Experience of Work-at-Home During a Pandemic: A Configuration Analysis

Understanding the User Experience of Work-at-Home During a Pandemic: A Configuration Analysis

Discontinuities in and Perceptions of Mental Health Service Path of Violent Young Offenders: A Qualitative Descriptive Study.

Studies on mental health service use among juvenile violent offenders prior to their acts of violence are sparse. Mostly, their service use seems to be short-term, although there may have been several service periods. Little is known about how they have perceived those services. Using a qualitative content analysis on data from forensic psychiatric examination statements, we studied discontinuities in the use of mental health services of 15-22-year-old violent Finnish offenders and descriptions of their perceptions of those services. There were several types of discontinuities: limited youth engagement and subsequent dropping out from services, or partial or total refusal of the proposed examinations or treatments. Most discontinuations were instigated by the youth themselves, followed by the parents and the service system. The subjects had perceived mental health services to be not beneficial for the most part, although some experienced benefits from medication. When treating children and adolescents with behavioral symptoms clinicians should identify the early signs of the process of disengagement from treatment and pay attention to the perceptions of the treatment of both the youth and their parents. Also, more research is needed on the user experience of mental health services among violent offenders, as well as factors relating to discontinuities along their mental health service path.

CmirC update 2024: a multi-omics database for clustered miRNAs

Clustered miRNAs consist of two or more miRNAs transcribed together and may coordinately regulate gene expression. Differential expression of clustered miRNAs is found to be controlled by crosstalk of genetic or epigenetic mechanisms. It has been demonstrated that clustered miRNA expression patterns greatly impact cancer cell progression. With the CmirC initiative, we initially developed a comprehensive database to identify copy number variation (CNV) driven clustered miRNAs in cancer. Now, we extended the analysis and identified three miRNAs, mir-96, mir-183, and mir-21, were found to be significantly upregulated in 17 cancer types. Further, CmirC is now upgraded to determine the impact of changes in the DNA methylation status at clustered miRNAs by utilizing The Cancer Genomic Atlas (TCGA) cancer datasets. We examined specific methylation datasets from 9,639 samples, pinpointing 215,435 methylation sites and 27,949 CpG islands with miRNA cluster information. The integrated analysis identified 34 clusters exhibiting differentially methylated CpG sites across 14 cancer types. Furthermore, we determined that CpG islands in the promoter region of 20 miRNA clusters could play a regulatory role. Along with ensuring a straightforward and convenient user experience, CmirC has been updated with improved data browsing and analysis functionalities, as well as enabled hyperlinks to literature and miR-cancer databases. The enhanced version of CmirC is anticipated to play an important role in providing information on the regulation of clustered miRNA expression, and their targeted oncogenes and tumor suppressors. The newly updated version of CmirC is available at https://slsdb.manipal.edu/cmirclust/.

Gesture recognition technology: a new dimension in human-computer interaction interface

Development of an interface for intelligent gesture control to improve user experience and increase the efficiency of interaction with a computer. This paper proposes a gesture recognition system based on artificial intelligence using convolutional neural networks (CNN). The system comprises three stages: pre-processing, optimal frame determination, and gesture category identification. The extracted features used are independent of movement, scaling, and rotation, providing greater flexibility to the system. The suggested gesture control technology, known as Kazakh Sign Language (KSL) for Kazakh alphabets, eliminates the need for additional devices, enabling users to interact with the system naturally. Experiments demonstrated that the proposed KSL system can accurately recognize Kazakh language alphabet letters with a high precision of 97.3%, owing to the utilization of artificial intelligence and CNN to enhance the accuracy and effectiveness of gesture control. Gestures, a type of visual formation, are perceivable by computers through machine learning models. The selection of methods and systems for recognizing Kazakh sign language gestures was accompanied by addressing various challenges related to language-specific orthographic and gestural features. The developed gesture control interface for human-computer interaction is applied in the field of inclusive education, aiming to assist deaf and hard-of-hearing children in learning sign language.

Assessment of a large language model based digital intelligent assistant in assembly manufacturing

The use of Digital Intelligent Assistants (DIAs) in manufacturing aims to enhance performance and reduce cognitive workload. By leveraging the advanced capabilities of Large Language Models (LLMs), the research aims to understand the impact of DIAs on assembly processes, emphasizing human-centric design and operational efficiency. The study is novel in considering the three primary objectives: evaluating the technical robustness of DIAs, assessing their effect on operators' cognitive workload and user experience, and determining the overall performance improvement of the assembly process. Methodologically, the research employs a laboratory experiment, incorporating a controlled setting to meticulously assess the DIA's performance. The experiment used a between-subjects design comparing a group of participants using the DIA against a control group relying on traditional manual methods across a series of assembly tasks. Findings reveal a significant enhancement in the operators' experience, a reduction in cognitive load, and an improvement in the quality of process outputs when the DIA is employed. The article contributes to the study of the DIA's potential and AI integration in manufacturing, offering insights into the design, development, and evaluation of DIAs in industrial settings.

A Review of Traffic Flow Prediction Models in 5G Using Machine Learning Techniques

Abstract: Wireless technology has advanced significantly since its origin and is now an essential aspect of our daily life. The advancement of wireless communication from the first generation (1G) to the fifth generation (5G) of technology has been revolutionary. An extensive summary of the development of wireless communication from 1G to 5G has been given in this paper. Cellular networks are heading towards becoming more diverse, broadband, integrated, and intelligent networks with the introduction of 5G networks. The goals of 5G wireless technology are to provide more users with more consistent user experiences, ultralow latency, vast network capacity, faster multi-Gbps peak data speeds and increased reliability. While the resources needed for computation and communication are also growing with the maturity of 5G technology .At the same time, cellular traffic has increased dramatically due to the widespread use of smart devices. Cellular traffic prediction is a crucial component of the resource management system for cellular networks but it confronts many difficulties due to strict standards for accuracy and dependability. Among the most important issues is how to enhance the predictive performance of Mobile data traffic. This review describes the need of traffic forecasting in cellular network in 5G technology. A study of different models for network analysis and traffic prediction by different researchers is presented in this paper. The distinctiveness and guidelines of earlier research for traffic prediction in 5G are examined. To determine the distinctive qualities of each method used for traffic prediction in mobile network, a thorough analysis of the most popular techniques using Machine learning for predictive analysis are discussed.

Enhancing Driving Safety through User Experience Evaluation of the C-ITS Mobile Application: A Case Study of the DARS Traffic Plus App in a Driving Simulator Environment

The paper evaluates the DARS Traffic Plus mobile application within a realistic driving simulator environment to assess its impact on driving safety and user experience, particularly focusing on the Cooperative Intelligent Transport Systems (C-ITS). The study is positioned within the broader context of integrating mobile technology in vehicular environments to enhance road safety by informing drivers about potential hazards in real time. A combination of experimental methods was employed, including a standardised user experience questionnaire (meCUE 2.0), measuring quantitative driving parameters and eye-tracking data within a driving simulator, and post-experiment interviews. The results indicate that the mobile application significantly improved drivers’ safety perception, particularly when notifications about hazardous locations were received. Notifications displayed at the top of the mobile screen with auditory cues were deemed most effective. The study concludes that mobile applications like DARS Traffic Plus can play a crucial role in enhancing road safety by effectively communicating hazards to drivers, thereby potentially reducing road accidents and improving overall traffic safety. Screen viewing was kept below the safety threshold, affirming the app’s efficacy in delivering crucial information without distraction. These findings support the integration of C-ITS functionalities into mobile applications as a means to augment older vehicle technologies and extend the safety benefits to a broader user base.

Key Characteristics and Perception of Different Outbreak Surveillance Systems in Côte d'Ivoire: Cross-Sectional Survey Among Users.

Accurate and timely infectious disease surveillance is pivotal for effective public health responses. An important component of this is the disease surveillance tools used. Understanding views and experiences of users is crucial for informing policy decisions and ensuring the seamless functioning of surveillance systems. In this study, we aimed to assess the user perceptions of 3 disease surveillance tools used in Côte d'Ivoire, namely, MAGPI, District Health Information Software 2 (DHIS2), and Surveillance Outbreak Response Management and Analysis System (SORMAS), the latter was implemented in 2021 within a pilot scheme. We conducted interviews and a web-based survey distributed to users of the 3 surveillance tools. The survey assessed users' views of the surveillance tools' usefulness, ease of use, feelings toward the tool, conditions that may influence the use, and other characteristics. The descriptive analysis compared responses from SORMAS, MAGPI, and DHIS2 users, providing a comprehensive evaluation of their experiences. Among the 159 respondents who actively use one of the systems, MAGPI was the most widely used surveillance tool among respondents (n=127, 79.9%), followed by DHIS2 (n=108, 67.9%), and SORMAS (n=25, 15.7%). In terms of users' perceptions, SORMAS, despite its limited implementation, emerged as a tool that allows for data analysis and had the most comprehensive set of functionalities. DHIS2 was appreciated for its frequency of report provision, although users reported occasional IT system failures. MAGPI was recognized for its ease of use but was reported to lack certain functionalities offered by the other surveillance systems. This study offers valuable insights into the perceptions of disease surveillance tools users in Côte d'Ivoire. While all systems were positively regarded, each exhibited strengths and weaknesses addressing different needs and functionalities. Policy makers and health officials can use these findings to enhance existing tools or consider a unified approach for infectious disease surveillance systems. Understanding users' perspectives allows them to optimize the choice of surveillance tools, ultimately strengthening public health responses in Côte d'Ivoire and potentially serving as a model for other countries facing similar decisions in their health care systems.

Quality of Experience That Matters in Gaming Graphics: How to Blend Image Processing and Virtual Reality

This paper investigates virtual reality (VR) technology which can increase the quality of experience (QoE) on the graphics quality within the gaming environment. The graphics quality affects the VR environment and user experience. To gather relevant data, we conduct a live user experience and compare games with high- and low-quality graphics. The qualitative feedback obtained through questionnaires prove the importance ofcontextualizing users’ experiences playing both games. Furthermore, our findings prove the crucial role of graphics quality in adopting user engagement and enjoyment during gaming sessions. Users consistently reported their feeling more connected when interacting with games and receiving high-quality graphics. If the graphics quality received is low, the user rating for a particular game is low. Further examination of VR technology reveals its potential to revolutionize graphics quality within game play.