Search Results (315)

This study investigates mechanisms that influence how users perceive a technology’s usefulness after adoption as they continue to use the technology. The Expectation Confirmation Model (ECM) has been widely used to examine the key drivers of IT continuance, emphasizing perceived usefulness as a central factor. Although researchers have explored factors, such as ease of use, trust, and site quality, affecting post-adoption perceived usefulness, the mechanisms shaping post-adoption perceived usefulness remain underexplored. This study proposes that post-adoption perceived usefulness is shaped through the actualization of the technology’s affordances. Using a survey focused on fitness app usage (e.g., Fitbit), we examined various affordances users actualize and whether actualization of an affordance shapes their perception of usefulness. Results show that some affordances are actualized widely by most users (e.g., exercise status updating) while others are actualized by fewer users (e.g., reminders to exercise or guiding users how to exercise). Moreover, when an affordance is widely actualized, it significantly influences users’ perceptions of usefulness within the ECM framework. Given that perceived usefulness is a key factor in predicting IT continuance, our findings contribute to the literature by highlighting the influence of actualized affordances on perceptions of usefulness and hence IT continuance. Full article

The introduction of mobile banking is essential in today’s financial sector, where technological innovation plays a critical role. To remain competitive in the current market, businesses must analyze client attitudes and perspectives, as these influence long-term demand and overall profitability. While previous studies have explored general adoption behaviors, limited research has examined how individual factors such as social influence, lifestyle compatibility, financial technology self-efficacy, and perceived usage cost affect mobile banking adoption among specific generational cohorts. This study addresses that gap by offering insights into these variables, contributing to the growing literature on mobile banking adoption, and presenting actionable recommendations for financial institutions targeting younger market segments. Using a structured questionnaire survey, data were collected from both users and non-users of mobile banking among the Gen Z population in the United States. The regression model significantly predicts mobile banking adoption, with an intercept of $-0.548$ ($p < 0.001$). Among the independent variables, perceived cost of usage has the strongest positive effect on adoption ($B=0.857$, $\beta =0.722$, $p < 0.001$), suggesting that adoption increases when mobile banking is perceived as more affordable. Social influence also has a significant positive impact ($B=0.642$, $\beta =0.643$, $p < 0.001$), indicating that peer influence is a central driver of adoption decisions. However, self-efficacy shows a significant negative relationship ($B=-0.343$, $\beta =-0.339$, $p < 0.001$), and lifestyle compatibility was found to be statistically insignificant ($p=0.615$). These findings suggest that reducing perceived costs, through lower fees, data bundling, or clearer communication about affordability, can directly enhance adoption among Gen Z consumers. Furthermore, leveraging peer influence via referral rewards, Partnerships with influencers, and in-app social features can increase user adoption. Since digital self-efficacy presents a barrier for some, banks should prioritize simplifying user interfaces and offering guided assistance, such as tutorials or chat-based support. Future research may employ longitudinal designs or analyze real-life transaction data for a more objective understanding of behavior. Additional variables like trust, perceived risk, and regulatory policies, not included in this study, should be integrated into future models to offer a more comprehensive analysis. Full article

To enhance the performance of online food delivery (OFD) services, it is essential to strengthen consumers’ intentions to use OFD apps, which are the core interface of this business. Accordingly, this study aims to identify the cognitive mechanisms that shape consumers’ intentions to use OFD apps and explore strategies to encourage their adoption. To achieve this, the study develops a research model that incorporates segmented dimensions of service convenience as key motivational factors, along with variables from the Technology Acceptance Model (TAM). A survey was conducted among OFD consumers in China, and the proposed research model was empirically tested using data from 478 valid responses. The analysis revealed that all six dimensions of service convenience serve as significant motivational drivers of OFD app usage. Furthermore, the study demonstrates that consumers’ coupon proneness and user-generated online reviews have significant moderating effects that reinforce the mechanism by which consumers adopt and use OFD apps. The findings and implications discussed in this study are expected to provide valuable insights and practical guidance for formulating effective strategies to promote more active consumer engagement with OFD apps in the future. Full article

There is a growing body of research showing that Alzheimer’s disease (AD) is related to enteric dysbacteriosis. Exercise can be effective in alleviating AD, but the effects that exercise has on the gut microbiota in AD patients needs to be further studied. Through this study, we aimed to investigate the differences in the diversity of gut microorganisms between AD model mice and wild-type mice and the effect that treadmill exercise has on the composition of the gut microbiota in both types of mice. C57BL/6 wild-type mice were randomly divided into a sedentary control group (WTC) and an exercise group (WTE); APP/PS1 double transgenic mice were also randomly divided into a sedentary control group (ADC) and an exercise group (ADE). After the control group remained sedentary for 12 weeks and a 12-week treadmill exercise intervention was adopted for the exercise group, the rectal contents were collected so that they could undergo V3-V4 16S rDNA sequencing, and a comparative analysis of the microbial composition and diversity was also performed. The alpha diversity of the gut microbiota in AD mice was lower than that in wild-type mice, but exercise increased the gut microbial diversity in both types of mice. At the phylum level, the dominant microorganisms in all four groups of mice were Bacteroidetes and Firmicutes. There was an increase in the Bacteroidetes phylum in AD mice. Treadmill exercise reduced the abundance of Bacteroidetes in both groups of mice, whereas the abundance of Firmicutes increased. At the genus level, Muribaculaceae, the Lachnospiraceae_NK4A136_group, Alloprevotella, and Alistipes were in relatively high abundance. Muribaculaceae and Alloprevotella were in greater abundance in AD mice than in wild-type mice, but both decreased after treadmill exercise. Through performing linear discriminant analysis effect size (LEfSe), we found that the dominant strains in AD mice were Campilobacterota, Helicobacteraceae, Escherichia–Shigella, and other malignant bacteria, whereas exercise resulted in an increase in probiotics among the dominant strains in both types of mice. Although gut microbial diversity decreases and malignant bacteria increase in AD mice, treadmill exercise can increase gut microbial diversity and lead to the development of dominant strains of probiotics in both types of mice. These findings provide a basis for applying exercise as a treatment for AD. Full article

Introduction: Adolescents and young adults (AYAs), especially those from underserved communities, often face barriers to sexual and reproductive health (SRH). This pilot study evaluated the implementation of mobile health technologies to promote SRH care, including the integration of the Rapid Adolescent Prevention Screening^TM (RAAPS) and the Health-E You/Salud iTu^TM (Health-E You) app at a School-Based Health Center (SBHC) in Los Angeles using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework. Methods: This multi-method pilot study included the implementation of an integrated tool with two components, the RAAPS electronic health screening tool and the Health-E You app, which delivers tailored SRH education and contraceptive decision support to patients (who were sex-assigned as female at birth) and provides an electronic summary to clinicians to better prepare them for the visit with their patient. Quantitative data on tool usage were collected directly from the back-end data storage for the apps, and qualitative data were obtained through semi-structured interviews and in-clinic observations. Thematic analysis was conducted to identify implementation barriers and facilitators. Results: Between April 2024 and June 2024, 60 unique patients (14–19 years of age) had a healthcare visit. Of these, 35.00% used the integrated RAAPS/Health-E You app, and 88.33% completed the Health-E You app only. All five clinic staff were interviewed and expressed that they valued the tools for their educational impact, noting that they enhanced SRH discussions and helped uncover sensitive information that students might not disclose face-to-face. However, the tools affected clinic workflows and caused rooming delays due to the time-intensive setup process and lack of integration with the clinic’s primary electronic medical record system. In addition, they also reported that the time to complete the screener and app within the context of a 30-min appointment limited the time available for direct patient care. Additionally, staff reported that some students struggled with the two-step process and did not complete all components of the tool. Despite these challenges, clinic staff strongly supported renewing the RAAPS license and continued use of the Health-E You app, emphasizing the platform’s potential for improving SRH care and its educational value. Conclusions: The integrated RAAPS and Health-E You app platform demonstrated educational value and improved SRH care but faced operational and technical barriers in implementing the tool. These findings emphasize the potential of such tools to address SRH disparities among vulnerable AYAs while providing a framework for future implementations in SBHCs. Full article

Objectives: Artificial intelligence (AI) symptom-checker apps are proliferating, yet their everyday usability and transparency remain under-examined. This study provides a triangulated evaluation of three widely used AI-powered mHealth apps: ADA, Mediktor, and WebMD. Methods: Five usability experts applied a 13-item AI-specific heuristic checklist. In parallel, thirty lay users (18–65 years) completed five health-scenario tasks on each app, while task success, errors, completion time, and System Usability Scale (SUS) ratings were recorded. A repeated-measures ANOVA followed by paired-sample t-tests was conducted to compare SUS scores across the three applications. Results: The analysis revealed statistically significant differences in usability across the apps. ADA achieved a significantly higher mean SUS score than both Mediktor (p = 0.0004) and WebMD (p < 0.001), while Mediktor also outperformed WebMD (p = 0.0009). Common issues across all apps included vague AI outputs, limited feedback for input errors, and inconsistent navigation. Each application also failed key explainability heuristics, offering no confidence scores or interpretable rationales for AI-generated recommendations. Conclusions: Even highly rated AI mHealth apps display critical gaps in explainability and error handling. Embedding explainable AI (XAI) cues such as confidence indicators, input validation, and transparent justifications can enhance user trust, safety, and overall adoption in real-world healthcare contexts. Full article

Patient-generated health data (PGHD) enhance traditional healthcare by enabling continuous monitoring and supporting personalized care, yet concerns over privacy, security, and integration into existing systems hinder broader adoption. This study examined the perceptions, awareness, and concerns of patients and caregivers regarding PGHD and assessed their willingness to share such data for clinical, research, and commercial purposes. A cross-sectional survey was conducted from 6 to 12 November 2023, involving 400 individuals with experience using PGHD. Participants completed structured questionnaires addressing health information management, PGHD usage, and attitudes toward its application. PGHD was most commonly used by patients with chronic conditions and guardians of minors, with tethered personal health record apps frequently utilized. Respondents identified improved self-management and better access to information as key benefits. However, significant concerns about data privacy and security emerged, especially regarding non-clinical use. Younger adults, particularly those in their 20s, showed lower willingness to engage with PGHD due to heightened privacy concerns. These findings suggest that, while support for clinical use of PGHD is strong, barriers related to trust and consent remain. Addressing privacy concerns and simplifying consent processes will be essential to promote equitable and responsible PGHD utilization across diverse patient populations. Full article

Determining the genetic variations of candidate genes in affected subjects will help identify early pathological biomarkers of Alzheimer’s disease (AD) and develop effective treatments. It has recently been found that some genes that are linked share an increase in intron retention (IR). In this review, we discuss a few instances of mRNA-IR in various genes linked to AD, including APOE, MAPT-Tau, Psen2, Farp1, Gpx4, Clu, HDAC4, Slc16a3, and App genes. These genes are vulnerable to IR, encompassing additional crucial proteins for brain functionality, but they are frequently involved in pathways linked to the control of mRNA and protein homeostasis. Despite the advancements in human in vivo RNA therapy, as far as we know, there are no reports of data generated regarding artificial in vivo splicing in either animal models or humans. To prevent genetic variations and improve or repair errors in expression of desired genes, humans have adopted new gene editing techniques like CRISPR-Cas9 and RNAi modalities. Ultimately, IR could be utilized as a therapeutic potential biomarker for disorders related to intronic expansion. Full article

Milk jujube has become an increasingly popular tropical fruit. The sugar content, which is commonly represented by the soluble solid content (SSC), is a key indicator of the flavor, internal quality, and market value of milk jujubes. Traditional methods for assessing SSC are time-consuming, labor-intensive, and destructive. These methods fail to meet the practical demands of the fruit market. A rapid, stable, and effective non-destructive detection method based on visible/near-infrared (Vis/NIR) spectroscopy is proposed here. A Vis/NIR reflectance spectroscopy system covering 340–1031 nm was constructed to detect SSC in milk jujubes. A structured spectral modeling framework was adopted, consisting of outlier elimination, dataset partitioning, spectral preprocessing, feature selection, and model construction. Comparative experiments were conducted at each step of the framework. Special emphasis was placed on the impact of outlier detection and dataset partitioning strategies on modeling accuracy. A data-augmentation-based unsupervised anomaly sample elimination (DAUASE) strategy was proposed to enhance the data validity. Multiple data partitioning strategies were evaluated, including random selection (RS), Kennard–Stone (KS), and SPXY methods. The KS method achieved the best preservation of the original data distribution, improving the model generalization. Several spectral preprocessing and feature selection methods were used to enhance the modeling performance. Regression models, including support vector regression (SVR), partial least squares regression (PLSR), multiple linear regression (MLR), and backpropagation neural network (BP), were compared. Based on a comprehensive analysis of the above results, the DAUASE + KS + SG + SNV + CARS + SVR model exhibited the highest prediction performance. Specifically, it achieved an average precision (AP_p) of 99.042% on the prediction set, a high coefficient of determination (R_P²) of 0.976, and a low root-mean-square error of prediction (RMSEP) of 0.153. These results indicate that Vis/NIR spectroscopy is highly effective and reliable for the rapid and non-destructive detection of SSC in milk jujubes, and it may also provide a theoretical basis for the practical application of rapid and non-destructive detection in milk jujubes and other jujube varieties. Full article

The rapid integration of artificial intelligence (AI) in education has transformed traditional teaching methodologies, particularly within Outcome-Based Education (OBE), in higher education. Based on the Technological Pedagogical Content Knowledge (TPACK) model and the OBE system, this present study investigates how teachers perceive AI applications, specifically ChatGPT, in enhancing instructional design and student performance. The research develops a new AI-based instructional planning model, incorporating AI ChatGPT capabilities, teacher competencies, and their direct and indirect effects on student outcomes. This study employs quantitative research design using Structural Equation Modeling (SEM) to validate the proposed model. Data were collected from 320 university teachers in Pakistan using a structured survey distributed through WhatsApp and email. Findings from the direct path analysis indicate that AI ChatGPT capabilities significantly enhance instructional planning (β = 0.33, p < 0.001) and directly impact student performance (β = 0.20, p < 0.001). Teacher competencies also play an important role in instructional planning (β = 0.37, p < 0.001) and student performance (β = 0.16, p = 0.020). The indirect path analysis reveals that instructional planning mediates the relationship between AI ChatGPT capabilities and student performance (β = 0.160, p < 0.001), as well as between teacher competencies and student performance (β = 0.180, p < 0.001). The R-square values indicate that instructional planning explains 41% of its variance, while student performance accounts for 56%. These findings provide theoretical contributions by extending AI adoption models in education and offer practical implications for integrating AI tools in teaching. This study emphasizes the need for professional development programs to enhance educators’ AI proficiency and suggests policy recommendations for AI-driven curriculum development. Full article

Background/Objectives: Effective communication between healthcare professionals and patient caregivers is paramount in the surgical waiting area, particularly during periods of heightened stress and emotional vulnerability. Globally, communication practices exhibit considerable variability, from traditional face-to-face interactions to integrating advanced digital technologies. Despite innovations, a comprehensive understanding of the impact of diverse communication strategies on patient caregiver satisfaction remains underdeveloped. This scoping review was designed to systematically map the existing literature on healthcare communication practices and identify strategies that may influence satisfaction among patient caregivers. Methods: A thorough search of multiple databases—Scopus, PubMed, CINAHL, Embase, ProQuest, Web of Science, the Cochrane Library, and clinical trial registries—was conducted. Only studies published in English or those for which an English full text was accessible were included. Eligible studies were those undertaken in hospital settings, including operating theaters, surgical units, surgical waiting areas, postoperative intensive care units, emergency departments, and other clinical areas focusing on patient caregivers. The review adhered to the methodological framework recommended by the Joanna Briggs Institute for scoping reviews and was reported following the most recent PRISMA-ScR guidelines. Results: Ultimately, five studies met the inclusion criteria. The selection process involved a structured search utilizing Medical Subject Headings (MeSH), keywords, and index terms, supplemented by manual reference list screening. Initial screening was performed based on titles and abstracts, followed by full-text evaluation using a standardized selection form. Data extraction focused on the communication methods, study designs, and outcomes related to patient caregiver satisfaction. The findings are synthesized narratively and presented through tables and figures, offering a comprehensive overview of global communication practices and their influence on patient caregiver satisfaction in surgical environments. Across the five included studies, digital communication interventions such as SMS, mobile apps, or video updates reported improved patient caregiver satisfaction (e.g., 70.8% in Canada and 97% in Switzerland) and also reduced patient caregiver anxiety (e.g., STAI score ≥ 44) in 74.2% of Ethiopian patient caregivers. Worldwide evidence highlights the practical importance of tailored digital communication practices to support providing timely and accessible information to patient caregivers, while also revealing gaps linked to insurance status, digital literacy, and various communication approaches in healthcare systems. Conclusions: The five studies included showed considerable variation in communication practices across surgical settings. The main findings reveal that structured, timely, and transparent communication, mainly via digital tools such as SMS updates and mobile applications, enhanced patient caregiver satisfaction and alleviated their emotional distress too. Nevertheless, gaps were identified in postoperative communication, and challenges, such as technological accessibility, digital literacy skills of patient caregivers, and inconsistent methods for measuring satisfaction outcomes, were noted across studies. This scoping review identified the different types of healthcare communication practices adopted globally in surgical care settings and also demonstrated their influence on patient caregiver satisfaction. Traditional and digital communication practices both have their significant impact on patient caregiver experiences in surgical healthcare settings, focusing more on timely and consistent real-time updates and culturally sensitive information. Addressing the existing communication gaps and having tailored communication approaches to specific contexts may lead to improved patient caregiver support and surgical outcomes. Full article

The surge in digital transformation within financial technology has catalyzed the development of super-apps—comprehensive mobile applications designed to serve a multitude of a customer’s daily needs on a single platform. Despite their widespread use, there is a dearth of research regarding customer adoption in the banking industry. Employing the integrated Information Systems (IS) success model, this study delves into how the characteristics of mobile banking super-apps influence user adoption intentions, taking into account various levels of service integration, in South Korea. The results reveal that factors such as interactivity, service diversity, process completeness, and technological service innovation positively affect the perceived ease of use. However, only service diversity and process completeness significantly influence perceived usefulness. Furthermore, distinct relationships between constructs are observed among different user groups based on their preferred service integration levels. This research can help banks formulate app management strategies and identify the optimal levels of service integration for their mobile banking super-apps. Full article

Introduction: The rapid growth of mobile health (mHealth) applications has revolutionized healthcare delivery worldwide. These digital tools encompass a broad array of functionalities, including telemedicine, appointment scheduling, medication management, and health data tracking, all of which contribute to enhanced healthcare accessibility, increased patient engagement, and improved operational efficiency. However, despite their increasing prominence, the design, deployment, and use of mHealth applications continue to face several challenges, such as usability issues and overall sustained adoption. Objectives: This study aims to evaluate mHealth applications in Saudi Arabia, focusing on their design characteristics, usability features, and current feature gaps. Method: A total of 21 mHealth applications were selected and analyzed using a thematic analysis approach. The apps were selected based on usage popularity in the Saudi market and relevance to national digital health strategies. Data were drawn from publicly available app store information, official app documentation, and expert evaluations. Results: The findings reveal that while mHealth applications excel in areas such as telemedicine, appointment booking, and health education, there are notable gaps in features such as behavior modification, patient monitoring, and health management. Conclusions: This study contributes to the growing body of research on mHealth by offering grounded insights into the functional landscape of digital health tools in Saudi Arabia. It also outlines practical recommendations to enhance usability, feature diversity, and alignment with evolving healthcare needs in Saudi Arabia and beyond. Full article

Background: Dietary intake plays a crucial role in health research, yet existing methods for its measurement can lead to participant burden, lengthy recording, and human errors, and do not account for age-specific variations. Libro is a real-time diet-tracking mobile-based app offering flexible features. An automated food recording program within Libro was customized for young people vulnerable to eating misbehaviour. This preliminary study assessed its relative validity using a self-administered 24 h recall method as the reference method. Methods: The relative validity of Libro was tested by adopting a cross-over design that recorded food intake over a period of 3 non-consecutive weekdays and 1 weekend day with both methods. The participants were recruited online through a mental health research charity, and this study was conducted fully online. The primary outcome was the concordance of total energy intake between the two methods, with secondary outcomes focusing on the intake of protein, carbohydrates, fats, free sugars, fibre, and trans-fatty acids. Test–retest validity was assessed per method with the intraclass correlation coefficient; a Bland–Altman plot and t-test were performed to test agreement at the group level; correlation coefficient and cross-classification were performed to assess agreement at the individual level. Results: Forty-seven participants were included in the final analysis. The average intraclass correlation coefficient for energy intake measured by Libro over four days was 0.85 (95% CI: 0.76–0.91). Compared to Intake24, the average energy intake recorded using Libro was significantly lower (mean difference: −554 Kcal, 95% CI: −804.1 to −305.6 Kcal, p < 0.001), potentially driven by the reduced reporting of foods rich in free sugars. The correlation coefficient for average energy intake measured by Libro vs. Intake24 was 0.32 (95% CI: 0.03, 0.55), with only 27.7% of subjects classified in the same quartile with both methods (κ = 0.31, 95% CI: −0.03, 0.55). Concordance varied across specific dietary component measures. Conclusions: While Libro had good test–retest reliability if adopting a multiple administration method, it underreported energy and other aspects of dietary intake, along with poor classification performance compared to Intake24 in a population vulnerable to eating misbehaviour. We suggest that future studies improve user experience to increase compliance and data accuracy. Full article

Our society is becoming increasingly dependent on technology, with immersive virtual worlds such as Extended Reality (XR) transforming how we connect and interact. XR technologies enhance communication and operational efficiency. They have been adopted in sectors such as manufacturing, education, and healthcare. However, the immersive and interconnected nature of XR introduces security risks that span from technical and human to psychological vulnerabilities. In this study, we examined security threats in XR environments through a scenario-driven risk assessment, using a hybrid approach combining Common Vulnerability Scoring System (CVSS) metrics and a custom likelihood model to quantify risks. This methodology provides a comprehensive risk evaluation method, identifying critical vulnerabilities such as Remote Code Execution (RCE), social engineering, excessive permission exploitation, unauthorized access, and data exfiltration. The findings reveal that human vulnerabilities, including users’ susceptibility to deception and excessive trust in familiar interfaces and system prompts, significantly increase attack success rates. Additionally, developer mode, once enabled, remains continuously active, and the lack of authentication requirements for installing applications from unknown sources, coupled with poor permission management on the part of the users, creates security gaps that attackers can exploit. Furthermore, permission management in XR devices is often broad and persistent and lacks real-time notifications, allowing malicious applications to exploit microphone, camera, and location access without the users knowing. By leveraging CVSS scores and a structured likelihood-based risk assessment, we quantified the severity of these threats, with RCE, social engineering, and insecure app installation emerging as the greatest risks. This study highlights the necessity of implementing granular permission controls, formalized developer mode restrictions, and structured user education programs to mitigate XR-specific threats. Full article