Search Results (212)

Background/Objectives: Adolescent idiopathic scoliosis (AIS) often requires prolonged brace use to prevent curve progression. However, adherence is challenging due to discomfort, mobility restrictions, and psychosocial stressors. This study evaluated the feasibility and clinical utility of a mobile health (mHealth) system for real-time tracking of brace adherence and treatment-related experiences in adolescents with AIS. Methods: Thirty adolescents with AIS (mean age = 12.9, SD = 1.8) undergoing brace treatment at a tertiary care center used a custom app for 90 days. The app collected daily self-reports on brace wear duration, discomfort, movement limitations, emotional distress, and social challenges. A clinical alarm system alerted providers when patient input indicated potential concerns. Primary outcomes were feasibility (adherence to daily use and usability ratings) and brace adherence. Secondary outcomes included the app’s capacity to identify treatment-related challenges and its association with changes in stress, quality of life, anxiety, and depression. Results: Participants reported meeting recommended brace wear time (≥16 h/day) on 84.8% of days. The app triggered 186 clinical alarms, with the most frequent related to emotional distress (23.1%) and pain (15.6%). Alarm frequency declined over time. Improvements of ≥20% in psychological outcomes were observed in 20–26.7% of participants, while group-level changes were nonsignificant. Conclusions: mHealth-based monitoring appears feasible and acceptable for digitally engaged adolescents with AIS. The app supported early detection of treatment barriers and prompted timely clinical responses. Despite limitations, it shows promise as a tool to improve treatment engagement and address psychosocial challenges in scoliosis care. Full article

Background: Theintegration of artificial intelligence (AI) into clinical decision support systems (CDSSs) has significantly enhanced diagnostic precision, risk stratification, and treatment planning. AI models remain a barrier to clinical adoption, emphasizing the critical role of explainable AI (XAI). Methods: This systematic meta-analysis synthesizes findings from 62 peer-reviewed studies published between 2018 and 2025, examining the use of XAI methods within CDSSs across various clinical domains, including radiology, oncology, neurology, and critical care. Model-agnostic techniques such as visualization models like Gradient-weighted Class Activation Mapping (Grad-CAM) and attention mechanisms dominated in imaging and sequential data tasks. Results: However, there are still gaps in user-friendly evaluation, methodological transparency, and ethical issues, as seen by the absence of research that evaluated explanation fidelity, clinician trust, or usability in real-world settings. In order to enable responsible AI implementation in healthcare, our analysis emphasizes the necessity of longitudinal clinical validation, participatory system design, and uniform interpretability measures. Conclusions: This review offers a thorough analysis of the state of XAI practices in CDSSs today, identifies methodological and practical issues, and suggests a path forward for AI solutions that are open, moral, and clinically relevant. Full article

Climate change caused by CO₂ emissions, the depletion of oil resources, and their unequivocal association with road transport constitute the primary factors behind the development of the electromobility sector. Simultaneously, existing infrastructure limitations and specific aspects of the social perception of electric vehicles may pose significant barriers to this sector’s growth in Poland, one of the fastest-growing economies in Europe. Therefore, this study aims to identify the level of diffusion of expert opinions regarding battery electric vehicles (BEVs) among vehicle users, in the context of user convenience (functionality) and their environmental impact, and to analyse the variability and determinants of these opinions. The obtained results are intended to serve as a basis for initiating actions to identify the limitations in the development of this automotive sector in Poland. Our study results indicate that the level of diffusion of expert opinions regarding BEVs among respondents is high. In contrast, opinions about these vehicles’ usability are more consistently internalised than those concerning their environmental impact. Moreover, this study demonstrates that limited financial resources and low levels of education among potential car buyers constitute barriers to developing this segment of the automotive market in Poland. Full article

Artificial intelligence (AI) plays a pivotal role in Industry 4.0, with predictive maintenance (PdM) emerging as a core application for improving operational efficiency by reducing unplanned downtime and extending asset life. Despite these advancements, the black-box nature of AI models remains a significant barrier to adoption, as industry stakeholders require systems that are both transparent and trustworthy. This study presents a systematic literature review examining how human-in-the-loop explainable AI (HITL-XAI) approaches can enhance the effectiveness and adoption of AI systems in PdM contexts. This review followed the PRISMA methodology, employing predefined search strings across Scopus, ProQuest, and EBSCO databases. Sixty-three peer-reviewed journal articles, published between 2019 and early 2025, were included in the final analysis. The selected studies span various domains, including industrial manufacturing, energy, and transportation, with findings synthesized through both descriptive and thematic analyses. A key gap identified is the limited empirical exploration of generative AI (GenAI) in improving the usability, interpretability, and trustworthiness of HITL-XAI systems in PdM applications. This review outlines actionable insights for integrating explainability and GenAI into existing rule-based PdM systems to support more adaptive and reliable maintenance strategies. Ultimately, the findings underscore the importance of designing HITL-XAI systems that not only demonstrate high model performance but are also effectively aligned with operational workflows and the cognitive needs of maintenance personnel. Full article

AI-powered full-site web generation tools promise to democratize website creation for novice users. However, their actual usability and accessibility for novice users remain insufficiently studied. This study examines interaction barriers faced by novice users when using Wix ADI to complete three tasks: Task 1 (onboarding), Task 2 (template customization), and Task 3 (product page creation). Twelve participants with no web design background were recruited to perform these tasks while their behavior was recorded via screen capture and eye-tracking (Tobii Glasses 2), supplemented by post-task interviews. Task completion rates declined significantly in Task 2 (66.67%) and 3 (33.33%). Help-seeking behaviors increased significantly, particularly during template customization and product page creation. Eye-tracking data indicated elevated cognitive load in later tasks, with fixation count and saccade count peaking in Task 2 and pupil diameter peaking in Task 3. Qualitative feedback identified core challenges such as interface ambiguity, limited transparency in AI control, and disrupted task logic. These findings reveal a gap between AI tool affordances and novice user needs, underscoring the importance of interface clarity, editable transparency, and adaptive guidance. As full-site generators increasingly target general users, lowering barriers for novice audiences is essential for equitable access to web creation. Full article

Background/Objectives: Poor classroom acoustics and inadequate digital environments in educational settings can pose an additional barrier for students, especially those with special needs, such as students with hearing difficulties. These challenges can hinder communication, academic achievement, and social inclusion. Speech-to-text captioning systems offer a promising assistive tool to support education. This study aimed to evaluate the strengths and limitations of implementing such systems in schools through a structured strategic analysis. Methods: The analysis method consisted of two phases. A SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis was performed on data from a survey compiled by an interdisciplinary team. A subsequent TOWS analysis was used to develop strategic recommendations by cross-referencing internal and external factors. Results: The analysis highlighted key strengths, including improved communication, support for inclusive practices, and adaptability to diverse learning needs. Identified weaknesses included cognitive load, synchronization delays, and variability in student profiles. Opportunities included educational innovation, access to funding programs, and interdisciplinary collaboration. Threats included inadequate classroom technology, poor acoustics, and the risks of social stigma. The analysis yielded 17 recommendations to improve the usability and customization of the tool. Conclusions: Speech-to-text captioning systems have significant potential to promote accessibility and inclusion in education. This strategic analysis provides a structured, interdisciplinary approach to strategic planning and the successful implementation of assistive technology in schools. By combining multidisciplinary expertise with structured evaluation, it identified key design, training, and policy priorities. This approach offers a replicable model for user-centered planning and the development of assistive tools and can inform wider efforts to reduce communication barriers in inclusive education. Full article

The construction industry is a cornerstone of New Zealand (NZ)’s economic growth, yet strategic infrastructure planning is constrained by fragmented and inconsistent pipeline data. Despite the increasing availability of construction pipeline datasets in NZ, their limited clarity, interoperability, and standardisation impede effective forecasting, policy development, and investment alignment. These challenges are compounded by disparate data structures, inconsistent reporting formats, and semantic discrepancies across sources, undermining cross-agency coordination and long-term infrastructure governance. To address this issue, the study begins by assessing the quality of four prominent pipeline datasets using Wang and Strong’s multidimensional data quality framework. This evaluation provides a necessary foundation for identifying the structural and semantic barriers that limit data integration and informed decision-making. The analysis examines four dimensions of data quality: accessibility, intrinsic quality, contextual relevance, and representational clarity. The findings reveal considerable inconsistencies in data fields, classification systems, and levels of detail across the datasets. Building on these insights, this study also develops a conceptual minimum dataset (MDS) framework comprising three core thematic categories: project identification, project characteristics, and project budget and timing. The proposed conceptual MDS includes unified data definitions, standardised reporting formats, and semantic alignment to enhance cross-platform usability and data confidence. This framework applies to the New Zealand context and is designed for replication in other jurisdictions, supporting the global push toward open, high-quality infrastructure data. The study contributes to the construction informatics and infrastructure planning by offering a practical solution to a critical data governance issue and introducing a transferable methodology for developing minimum data standards in the built environment to enable more informed, coordinated, and evidence-based decision-making. Full article

Depression represents one of the most prevalent mental health disorders globally, significantly impacting quality of life and posing substantial healthcare challenges. Traditional diagnostic methods rely on subjective assessments and clinical interviews, often leading to misdiagnosis, delayed treatment, and suboptimal outcomes. Recent advances in biosensing technologies offer promising avenues for objective depression assessment through detection of relevant biomarkers and physiological parameters. This review examines multi-modal biosensing approaches for depression by analyzing electrochemical biosensors for neurotransmitter monitoring alongside wearable sensors tracking autonomic, neural, and behavioral parameters. We explore sensor fusion methodologies, temporal dynamics analysis, and context-aware frameworks that enhance monitoring accuracy through complementary data streams. The review discusses clinical validation across diagnostic, screening, and treatment applications, identifying performance metrics, implementation challenges, and ethical considerations. We outline technical barriers, user acceptance factors, and data privacy concerns while presenting a development roadmap for personalized, continuous monitoring solutions. This integrative approach holds significant potential to revolutionize depression care by enabling earlier detection, precise diagnosis, tailored treatment, and sensitive monitoring guided by objective biosignatures. Successful implementation requires interdisciplinary collaboration among engineers, clinicians, data scientists, and end-users to balance technical sophistication with practical usability across diverse healthcare contexts. Full article

As the global population ages, older adults face growing psychological challenges such as loneliness, cognitive decline, and loss of social roles. Meanwhile, artificial intelligence (AI) technologies, including chatbots and voice-based systems, offer new pathways to emotional support and mental stimulation. However, older adults often encounter significant barriers in accessing and effectively using AI tools. This review examines the current landscape of AI applications aimed at enhancing psychological well-being among older adults, identifies key challenges such as digital literacy and usability, and highlights design and training strategies to bridge the digital divide. Using socioemotional selectivity theory and technology acceptance models as guiding frameworks, we argue that AI—especially in the form of conversational agents—holds transformative potential in reducing isolation and promoting emotional resilience in aging populations. We conclude with recommendations for inclusive design, participatory development, and future interdisciplinary research. Full article

The adoption of digital technologies, such as the Internet of Things (IoT), has emerged as a key strategy to improve efficiency, sustainability, and productivity in the agricultural sector, especially in contexts of modernization and digital transformation in developing regions. This study analyzes the key factors influencing the adoption of IoT technologies by farmers in the province of Guayas, Ecuador, and their impact on agricultural yields. The research is grounded in innovation diffusion theory and technology acceptance models, which emphasize the role of perception, usability, training, and economic viability in digital adoption. A total of 250 surveys were administered, with 232 valid responses (92.8% response rate), reflecting strong interest from the agricultural sector in digital transformation and precision agriculture. Using structural equation modeling (SEM), the results confirm that general perception of IoT (β = 0.514), practical functionality (β = 0.488), and technical training (β = 0.523) positively influence adoption, while high implementation costs negatively affect it (β = −0.651), all of which are statistically significant (p < 0.001). Furthermore, adoption has a strong positive effect on agricultural yield (β = 0.795). The model explained a high percentage of variance in both adoption (R² = 0.771) and performance (R² = 0.706), supporting its predictive capacity. These findings underscore the need for public and private institutions to implement targeted training and financing strategies to overcome economic barriers and foster the sustainable integration of IoT technologies in Ecuadorian agriculture. Full article

Integrating blockchain into healthcare devices offers the potential for improved data control but faces significant usability and acceptance challenges. This study addresses this gap by evaluating CipherPal, an improved blockchain-enabled Smart Fidget Toy prototype, using a multi-framework approach to understand the interplay between technology, design, and user experience. We synthesized insights from three complementary frameworks: an expert review assessing adherence to Web3 Design Guidelines, a User Acceptance Toolkit assessment with professionals based on UTAUT2, and an extended three-day user testing study. The findings revealed that users valued CipherPal’s satisfying tactile interaction and perceived benefits for well-being, such as stress relief. However, significant usability barriers emerged, primarily related to challenging device–application connectivity and data synchronization. The multi-framework approach proved valuable in revealing these core tensions. While the device was conceptually accepted, the blockchain integration added significant interaction friction that overshadowed its potential benefits during the study. This research underscores the critical need for user-centered design in health-related blockchain applications, emphasizing that seamless usability and abstracting technical complexity are paramount for adoption. Full article

Background/Objectives: Telehealth enhances access to specialty care, but stakeholder perspectives are often overlooked. The objective was to evaluate participant and provider satisfaction with a novel telehealth-enabled home safety program. Methods: This is a sub-investigation of a non-randomized pilot study of a novel telehealth-enabled home safety program that enrolled 23 persons with Parkinson’s Disease (PwPs) at risk for falls and their respective care partners (CPs). Dyads participated in four to six televisits over three months, where they performed “virtual home tours” using a mobile platform (tablet mounted on a rolling stand) with a physical therapist to identify and mitigate environmental fall hazards. Satisfaction was assessed using PI-developed surveys and open feedback. Mobile platform usability was assessed with the System Usability Scale (SUS). Results: A total of 95.65% of dyads were very to extremely satisfied with the entire program overall, and the therapist indicated the same for 73.91% of the dyads. Additionally, 95.65% of dyads reported gaining new awareness of home fall hazards. Difficulties maneuvering the mobile platform, using a tablet, and connectivity issues were common challenges noted. The mean score on SUS for the mobile platform was 65, indicating poor perceived usability, and most dyads indicated they would have preferred using a smartphone for the program. Other priorities, including competing health and personal obligations, along with resistance to change, were the primary barriers to implementing program recommendations. Conclusions: Our novel telehealth-enabled home safety program was well-received by patients and the study therapist. Using a smartphone and troubleshooting connectivity issues might help further improve the usability and accessibility of this program. Full article

Background: Caregivers of children often encounter barriers when accessing pediatric healthcare services. These challenges highlight the need for digital innovations to improve accessibility and efficiency in pediatric outpatient care. Objectives: This study aimed to design, implement, and pilot evaluate the Smart Well Child Center application in conjunction with enhancements to the Pediatric Outpatient Department. Methods: This study employs a mixed-methods research approach. The application was developed following the system development life cycle (SDLC) process, and its performance was subsequently evaluated. Additionally, its effectiveness in real-world settings was assessed through a satisfaction survey completed by 85 child caregivers. The results were summarized using the mean and standard deviation, and satisfaction levels were compared using paired t-test and repeated measures ANOVA. Results: The findings reveal that caregivers face significant challenges, including financial burdens related to travel, prolonged wait times, and difficulties accessing healthcare services. In response, the application was designed to incorporate key functionalities. Within the pre-consultation self-assessment module, caregivers can complete evaluations and receive recommendations directly through the application. Furthermore, the service procedure flowchart was restructured to seamlessly integrate these digital innovations, thereby enhancing the overall healthcare experience. The evaluation results indicate that the application achieved high performance ratings across all assessed dimensions (4.06 ± 0.77). Additionally, caregivers reported a substantial increase in satisfaction levels both immediately after implementation (4.58 ± 0.57) and one month afterward (4.59 ± 0.33). Conclusions: Given these findings, it is recommended that the hospital fully adopt the Smart Well Child Center application to improve healthcare accessibility and reduce patient wait times. Future research should assess the long-term impact of the intervention on both caregiver outcomes and healthcare professional workflow, satisfaction, and system usability, to inform broader implementation strategies. Full article

Background: Data accessibility remains a significant barrier in healthcare AI due to privacy constraints and logistical challenges. Synthetic data, which mimics real patient information while remaining both realistic and non-identifiable, offers a promising solution. Large Language Models (LLMs) create new opportunities to generate high-fidelity clinical conversations between patients and physicians. However, the value of this synthetic data depends on careful evaluation of its realism, accuracy, and practical relevance. Objective: To assess the performance of four leading LLMs: ChatGPT 4.5, ChatGPT 4o, Claude 3.7 Sonnet, and Gemini Pro 2.5 in generating synthetic transcripts of patient–physician interactions in plastic surgery scenarios. Methods: Each model generated transcripts for ten plastic surgery scenarios. Transcripts were independently evaluated by three clinically trained raters using a seven-criterion rubric: Medical Accuracy, Realism, Persona Consistency, Fidelity, Empathy, Relevancy, and Usability. Raters were blinded to the model identity to reduce bias. Each was rated on a 5-point Likert scale, yielding 840 total evaluations. Descriptive statistics were computed, and a two-way repeated measures ANOVA was used to test for differences across models and metrics. In addition, transcripts were analyzed using automated linguistic and content-based metrics. Results: All models achieved strong performance, with mean ratings exceeding 4.5 across all criteria. Gemini 2.5 Pro received mean scores (5.00 ± 0.00) in Medical Accuracy, Realism, Persona Consistency, Relevancy, and Usability. Claude 3.7 Sonnet matched the scores in Persona Consistency and Relevancy and led in Empathy (4.96 ± 0.18). ChatGPT 4.5 also achieved perfect scores in Relevancy, with high scores in Empathy (4.93 ± 0.25) and Usability (4.96 ± 0.18). ChatGPT 4o demonstrated consistently strong but slightly lower performance across most dimensions. ANOVA revealed no statistically significant differences across models (F(3, 6) = 0.85, p = 0.52). Automated analysis showed substantial variation in transcript length, style, and content richness: Gemini 2.5 Pro generated the longest and most emotionally expressive dialogues, while ChatGPT 4o produced the shortest and most concise outputs. Conclusions: Leading LLMs can generate medically accurate, emotionally appropriate synthetic dialogues suitable for educational and research use. Despite high performance, demographic homogeneity in generated patients highlights the need for improved diversity and bias mitigation in model outputs. These findings support the cautious, context-aware integration of LLM-generated dialogues into medical training, simulation, and research. Full article

Background/Objectives: Adherence to healthy behaviors after open-heart surgery is crucial for recovery and long-term health. Traditional patient education methods can be enhanced by using technology to improve engagement and self-care. This study aimed to develop and assess the feasibility of the “Term-Jai” smartphone application for promoting healthy heart behaviors in open-heart surgery patients. Methods: The “Term-Jai” psychological theory-based application was tested quantitatively and qualitatively over a 30-day period with 13 patients (age 44–78 years) following open-heart surgery between November 2023 and March 2024. Participant engagement, healthy behaviors, user experience, and usability were assessed using the System Usability Scale (SUS), satisfaction ratings, healthy behavior questionnaires, and semi-structured interviews. Results: The application was feasible, with 70% of participants remaining engaged during the intervention. The average SUS score was 80.2 ± 10.3, indicating good usability. Participants found the application’s information useful, clear, and easy to understand, showing improvements in health behaviors following application usage. The qualitative analysis highlighted the application’s intuitive design and potential for supporting cardiac rehabilitation. High satisfaction scores suggested its effectiveness despite some barriers to application usage around technical support and personalized exercise progression. Conclusions: The “Term-Jai” application is a promising tool for promoting healthy behaviors in patients following open-heart surgery. The application shows good usability and participant satisfaction, indicating its potential for broader implementation after further refinements. Full article