Search Results (658)

Sustainable urban transportation relies on safe interactions between motor vehicles and vulnerable road users (VRUs) such as bicyclists and pedestrians. This study evaluates how drivers’ risk-taking and emotional intelligence (EI) influence their interactions with VRUs in urban environments. A driving simulator study with 40 participants examined nine bicycle-passing events and one pedestrian-crossing scenario. The results show that higher risk-taking is significantly associated with more hazardous behaviors: each unit increase in risk-taking predicted a 4.02 mph higher passing speed and a 60% lower likelihood of braking for pedestrians. Event context also shaped behavior: drivers reduced their speed by 2.52 mph when passing cyclists on the road and by 2.33 mph for groups of cyclists, compared to single cyclists in bike lanes. Across all risk categories, the participants expressed discomfort when sharing the road, preferring to pass bicyclists on sidewalks, although the ‘risk-avoidant’ group reported significant discomfort even in these scenarios. EI did not significantly predict driving outcomes, likely reflecting limited score variability rather than an absence of influence. These insights support sustainable urban mobility by informing risk-based driver training and safer infrastructure design. Improving driver–VRU interactions helps create safer streets for walking and cycling, an essential condition for reducing car dependence and advancing sustainable transportation systems. Full article

In the era of big data, the rapid proliferation of user-generated content enriched with geolocations offers new perspectives and datasets for probing the spatiotemporal dynamics of tourist mobility. Mining large-scale geospatial traces has become central to tourism geography: it reveals preferences for attractions and routes to enable intelligent recommendation, enhance visitor experience, and advance smart tourism, while also informing spatial planning, crowd management, and sustainable destination development. Using Mount Huangshan—a UNESCO World Cultural and Natural Heritage site—as a case study, we integrate GPS trajectories and geo-tagged photographs from 2017–2023. We apply a Density-Field Hotspot Detector (DF-HD), a Space–Time Cube (STC), and spatial gridding to analyze behavior from temporal, spatial, and fully spatiotemporal perspectives. Results show a characteristic “double-peak, double-trough” seasonal pattern in the number of GPS tracks, cumulative track length, and geo-tagged photos. Tourist behavior exhibits pronounced elevation dependence, with clear vertical differentiation. DF-HD efficiently delineates hierarchical hotspot areas and visitor interest zones, providing actionable evidence for demand-responsive crowd diversion. By integrating sequential time slices with geography in a 3D framework, the STC exposes dynamic spatiotemporal associations and evolutionary regularities in visitor flows, supporting real-time crowd diagnosis and optimized spatial resource allocation. Comparative findings further confirm that Huangshan’s seasonal intensity is significantly lower than previously reported, while the high agreement between trajectory density and gridded photos clarifies the multi-tier clustering of route popularity. These insights furnish a scientific basis for designing secondary tour loops, alleviating pressure on core areas, and charting an effective pathway toward internal structural optimization and sustainable development of the Mount Huangshan Scenic Area. Full article

Mobile learning (m-learning) technologies are gaining popularity in universities but not uniformly across institutions because of cognitive, affective, and behavior obstacles. This research tested and applied an expansion of the Technology Acceptance Model (TAM) with technostress (TECH) and resistance to change (RTC) as affective obstacles, as well as the core predictors of perceived usefulness (PU), perceived ease of use (PE), and perceived risk (PR). By employing a cross-sectional survey of Greek university students (N = 608) and partial least squares structural equation modeling (PLS-SEM), we tested direct and indirect impacts on behavioral intention (BI) to apply m-learning applications. The results affirm that PU and PE are direct predictors of BI, while PR has no direct impact on BI but acts indirectly through TECH and RTC. Mediation is partial in terms of PE and PU and indirect-only (complete) in terms of PR with respect to the impact of affective states on adoption. Multi-group comparisons found differences in terms of gender, age, confidence, and years of use but not frequency of use, implying that psychological and experiential characteristics have a greater impact on intention than habitual patterns. These results offer theory-driven and segment-specific guidelines for psychologically aware, user-focused m-learning adoption in higher education. Full article

The rapid evolution of human–technology interaction necessitates continuous sensorimotor adaptation to new digital interfaces and tasks. Mobile typing, defined as text entry on smartphone touchscreens, offers a compelling example of this process, requiring users to adapt fine motor control and coordination to a constrained virtual environment. Aligned with the embodied cognition framework, understanding these digital sensorimotor experiences is crucial. A key theoretical question is whether these skills primarily involve adaptation of existing motor patterns or necessitate de novo learning, a distinction particularly relevant across generations with differing early sensorimotor experiences. This narrative review synthesizes current understanding of the sensorimotor aspects of smartphone engagement and typing skill evaluation methods. It examines touchscreen competence, skill acquisition, diverse strategies employed, and the influence of interface constraints on motor performance, while also detailing various sophisticated performance metrics and analyzing different data collection methodologies. Research highlights that analyzing typing behaviors and their underlying neural correlates increasingly serves as a potential source of behavioral biomarkers. However, while notable progress has been made, the field is still developing, requiring stronger methodological foundations and crucial standardization of metrics and protocols to fully capture and understand the dynamic sensorimotor processes involved in digital interactions. Nevertheless, mobile typing emerges as a compelling model for advancing our understanding of human sensorimotor learning and cognitive function, offering a rich, ecologically valid platform for investigating human-world interaction. Full article

Background/Objectives: Coupling mobile health (mHealth) technology with community-based nutrition programs may enhance diet quality in adolescents. This pilot study evaluated the feasibility, acceptability, and preliminary efficacy of integrating PortionSize Ed (PSEd), an image-assisted dietary assessment and education app, into the six-week Hawaii Food and Lifeskills for Youth (HI-FLY) curriculum delivered via Supplemental Nutrition Assistance Program Education (SNAP-Ed). Methods: Adolescents (grades 6–8) from two classrooms were cluster-randomized into HI-FLY or HI-FLY + PSEd. Both groups received HI-FLY and completed Youth Questionnaires (YQ) and food records (written or app-based) at Weeks 0 and 7. Feasibility and acceptability were assessed via enrollment, attrition, and User Satisfaction Surveys (USS). Diet quality was measured using Healthy Eating Index-2020 (HEI-2020) scores and analyzed via mixed-effects models. Results: Of 50 students, 42 (84%) enrolled and attrition was minimal (2.4%). The sample was 49% female and 85% at least part Native Hawaiian or Pacific Islander (NHPI). PSEd was acceptable, with average USS scores above the scale midpoint. No significant HEI-2020 changes were observed, though YQ responses indicated improvements in sugary drink intake (p = 0.03) and use of nutrition labels in HI-FLY + PSEd (p = 0.0007). Conclusions: Integrating PSEd into SNAP-Ed was feasible, acceptable, and demonstrated potential healthy behavior change among predominantly NHPI youth in Hawaii. Full article

Background: In an era of unprecedented technology adoption in healthcare, it is imperative to understand and predict factors influencing users’ perspective. This study employs a risk-integrated technology acceptance model aiming to identify the determinants of the intention to use mobile health applications among patients with chronic diseases in Romania. Methods: A face-to-face survey method was used to collect research data from 207 subjects, and the partial least squares structural equation modeling approach was employed for data analysis. Results: The behavioral intention to use mobile health applications (INT) was influenced positively by the perceived ease of use (PEOU, f² = 0.358, β = 0.500, p < 0.001) and perceived usefulness (PU, f² = 0.271, β = 0.678, p < 0.001). Another core predictor, with a negative effect on the intention to use, was the user’s perceived risk of using the technology (RISK, f² = 0.239, β = −0.321, p < 0.001), in turn influenced by the perceived degree of cyber-insecurity (CYBER, f² = 0.492, β = 0.639, p < 0.001). Digital self-efficacy (DSE) was identified as an external determinant with strong positive influence on PEOU (f² = 0.486, β = 0.610, p < 0.001). The model shows strong performance, reflected in a high Tenenhaus goodness-of-fit index (0.770) and solid explanatory power for the outcome variable (adjusted R² = 0.718). Conclusions: This study validates an extended risk-integrated technology acceptance model, offering robust insights into the determinants of mobile health application adoption among chronic patients in Romania. The findings provide actionable guidance for designing targeted interventions and healthcare policies to enhance technology adoption in this population. Full article

Mobile Cognitive Radio Networks (MCRNs) have emerged as a promising solution to address spectrum scarcity by enabling dynamic access to underutilized frequency bands assigned to Primary or Licensed Users (PUs). These networks rely on Cooperative Spectrum Sensing (CSS) to identify available spectrum, but this collaborative approach also introduces vulnerabilities to security threats—most notably, Spectrum Sensing Data Falsification (SSDF) attacks. In such attacks, malicious nodes deliberately report false sensing information, undermining the reliability and performance of the network. This paper investigates the application of machine learning techniques to detect and mitigate SSDF attacks in MCRNs, particularly considering the additional challenges introduced by node mobility. We propose a hybrid detection framework that integrates a reputation-based weighting mechanism with Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers to improve detection accuracy and reduce the influence of falsified data. Experimental results on software defined radio (SDR) demonstrate that the proposed method significantly enhances the system’s ability to identify malicious behavior, achieving high detection accuracy, reduces the rate of data falsification by approximately 5–20%, increases the probability of attack detection, and supports the dynamic creation of a blacklist to isolate malicious nodes. These results underscore the potential of combining machine learning with trust-based mechanisms to strengthen the security and reliability of mobile cognitive radio networks. Full article

Providing age-friendly metro service substantially enhances the elderly’s mobility and well-being. Despite recent progress in user profiling and mobility prediction, the prediction of the elderly’s metro travel patterns remains limited. To fill this gap, this study proposes a framework integrating user profiling and knowledge graph embedding to predict the elderly’s activity types at metro trip destinations, utilizing 180,143 smart card records and 885,072 points of interest (POI) records from Chongqing, China in 2019. First, an elderly metro travel profile (EMTP) tag system is developed to capture the elderly’s spatiotemporal metro travel behaviors and preferences. Subsequently, an elderly metro travel knowledge graph (EMTKG) is constructed to support semantic reasoning, transforming the activity types prediction problem into a knowledge graph completion problem. To solve the completion problem, the Temporal and Non-Temporal ComplEx (TNTComplEx) model is introduced to embed entities and relations into a complex vector space and distinguish between time-sensitive and time-insensitive behavioral patterns. Fact plausibility within the graph is evaluated by a scoring function. Numerical experiments validate that the proposed model outperforms the best-performing baselines by 13.37% higher Accuracy@1 and 52.40% faster training time per epoch, and ablation studies further confirm component effectiveness. This study provides an enlightening and scalable approach for enhancing age-friendly metro system service. Full article

Background/Objectives: As global aging accelerates, there is a pressing and empirically substantiated demand for integrated and sustainable strategies, as evidenced by the rising prevalence rates of chronic conditions, social isolation, and digital exclusion among older adults worldwide. These factors underscore the urgent need for multidimensional interventions that simultaneously target physical, psychological, and social well-being. The HOPE-FIT (Hybrid Outreach Program for Exercise and Follow-up Integrated Training) model and the SAGE (Senior Active Guided Exercise) program were designed to address this need through a hybrid framework. These programs foster inclusive aging by explicitly bridging digitally underserved groups and mobility-restricted populations into mainstream health promotion systems through tailored exercise, psychosocial support, and smart-home technologies, thereby functioning as a scalable meta-model across healthcare, community, and policy domains. Methods: HOPE-FIT was developed through a formative, multi-phase process grounded in the RE-AIM framework and a Hybrid Type II effectiveness–implementation design. The program combines professional health coaching, home-based and digital exercise routines, Acceptance and Commitment Performance Training (ACPT)-based psychological strategies, and smart-home monitoring technologies. Empirical data from pilot studies, large-scale surveys (N = 1000), and in-depth user evaluations were incorporated to strengthen validity and contextual adaptation. Culturally tailored content and participatory feedback from older adults further informed ecological validity and program refinement. Implementation Strategy/Framework: The theoretical foundation integrates implementation science with behavioral and digital health. The RE-AIM framework guided reach, fidelity, and maintenance planning, while the Hybrid E–I design enabled the concurrent evaluation of effectiveness outcomes and contextual implementation strategies. Institutional partnerships with community centers, public health organizations, and welfare agencies further facilitated the translation of the model into real-world aging contexts. Dissemination Plan: The multi-pronged dissemination strategy includes international symposia, interdisciplinary academic networks, policy briefs, localized community deployment, and secure, authenticated data sharing for reproducibility. This design facilitates evidence-informed policy, empowers practitioners, and advances digital health equity. Ultimately, HOPE-FIT constitutes a scalable and inclusive model that concretely addresses health disparities and promotes active, dignified aging across systems and disciplines. Full article

Background: Prolonged sedentary behavior and associated obesity are recognized risk factors for poor health across the lifespan. Globally, data show that many children and adolescents aged 5 to 17 significantly increased their sedentary behaviors during the COVID-19 pandemic, failing to meet recommended physical activity levels and reporting increased smartphone use. While mobile devices and video games have been traditionally linked to physical inactivity, formats like exergaming, which combine gameplay with gross motor activity, offer potential to promote physical activity. However, many digital health tools for children are developed without incorporating feedback from key stakeholders and end-users (e.g., children, teachers, and guardians). Therefore, this paper, within the Walk around the Earth (E-Walk) project, describes a prospective study that aims (1) to identify the most influential factors or characteristics affecting engagement with and usability of a mobile application promoting physical activity among primary school students; (2) to develop a mobile application for children based on the identified factors and characteristics. Methods: This project will use a group concept mapping approach to identify the most influential features/factors/characteristics affecting engagement with and usability of an app. By involving primary stakeholders (e.g., children, teachers, guardians, and physical activity experts), the project seeks to align the app’s features with primary end-user needs and motivations. Following the app’s development, its effectiveness in increasing physical activity levels and reducing sedentary behaviors will be evaluated through a mixed-method design, incorporating anthropometric data, validated physical activity questionnaires (Physical Activity Questionnaire for Older Children (PAQ-C) and International Physical Activity Questionnaire (IPAQ)), and engagement metrics. Conclusions: The E-Walk project integrates participatory design with educational content and activity-based challenges, representing a multidimensional strategy for promoting health and learning in primary school students. Ultimately, this study contributes to the development of user-informed digital interventions that support sustainable behavioral changes, in line with broader goals of child well-being and digital health promotion. Full article

In the post-disaster recovery of power distribution networks (PDNs), electric vehicles (EVs) possess a great potential as mobile energy storage units. When supported by vehicle-to-grid (V2G)-enabled charging stations, EVs can provide effective supplementary power for disaster-stricken areas. However, most existing stations only support unidirectional charging, limiting the resilience-enhancing potential of V2G. To address this gap, this paper proposes a resilience-oriented restoration optimization model that jointly considers the siting of V2G-enabled charging stations and PDN topology reconfiguration. A novel metric—Resilience Entropy—is introduced to dynamically characterize the recovery process. The model explicitly describes fault propagation and circuit breaker operations, while incorporating power flow and radial topology constraints to ensure secure operation. EV behavioral uncertainty is also considered to enhance model adaptability under real-world post-disaster conditions. The optimal siting scheme is obtained by solving the proposed model. Case studies demonstrate the model’s effectiveness in improving post-disaster supply and recovery efficiency, and analyze the impact of user participation willingness on V2G-based restoration. Full article

Augmented reality (AR) technologies—such as Instagram filters—bridge the digital and physical worlds by allowing users to virtually try on clothing, thereby reducing the risk of virus transmission. In the T-shirt retail industry, AR enables product personalization, decreases the need for physical production, minimizes textile waste, and lowers carbon emissions. It also benefits individuals with limited mobility or those who prefer shopping online. This study tested several hypotheses on 105 active Instagram filter users using filters from the ’Apprecio’ account on mobile devices. Data analyzed using the partial least squares method revealed that interactivity significantly influences both purchase intention and continued use of digital platforms. While hedonic and vivid features enhance the user experience, they have a limited impact on driving purchases or long-term engagement. Customers’ engagement and buying intent are more strongly shaped by practical and interactive elements. The study recommends that companies invest in developing interactive AR features to boost customer satisfaction and foster trust. Future research should involve larger participant samples and investigate specific interactive elements—such as virtual try-on tools—to better understand their impact on consumer behavior. This study highlights the critical role of interactivity in AR for delivering meaningful and engaging shopping experiences. Full article

As a key element of the sharing economy, ride-sharing plays a vital role in promoting sustainable urban mobility by optimizing vehicle utilization rates, lowering carbon emissions, and alleviating traffic congestion. Despite its cost-efficiency and sustainability benefits, ride-sharing adoption remains limited in the post-pandemic period due to behavioral changes and safety concerns. Accordingly, using survey data from 425 commuters in Hefei, concerns about COVID-19 and satisfaction with ride-sharing services were integrated into the theory of planned behavior framework. Structural equation modeling was applied to examine the relationship between ride-sharing intention and actual usage behaviors. The results indicated that ride-sharing intention was significantly positively affected by subjective norms (β = 0.428 ***), service satisfaction (β = 0.315 ***), and perceived behavioral control (β = 0.162 *), but significantly negatively affected by concerns about COVID-19 (β = −0.183 **). Concerns about COVID-19 significantly negatively affected travelers’ actual ride-sharing behaviors (β = −0.2 **). Furthermore, ride-sharing intention was identified as a significant positive predictor of travelers’ behaviors: specifically, their likelihood of accepting a ride-sharing order (β = 0.395 ***). These findings offer transport authorities evidence-based strategies for designing targeted interventions during health crises, particularly through reinforcing social norms, improving service quality, and implementing transparent health protocols to ensure both user safety and sustainability. Full article

Background: A point prevalence survey conducted at Singapore General Hospital in 2021 showed 48% of inpatients on antibiotics. We hypothesize that a mobile application, transforming complex antibiotic prescribing information into a succinct and individualized resource, will empower healthcare professionals and improve antibiotic prescriptions. Hence, we developed ABxSG using the behavioral design thinking approach (BDTA) to ensure positive user experience and sustained engagement. We aim to evaluate the impact of ABxSG on the proportion of inpatients on antibiotics, antibiotic appropriateness, and the number of antibiotic-related interventions by pharmacists. Methods: ABxSG was launched in March 2023. An interrupted time series analysis was conducted to evaluate its impact on the above outcomes measured using data collected from October 2021 to September 2024. There were 18 data points pre- and post-ABxSG. Results: Following the ABxSG launch, there was an immediate reduction in the proportion of inpatients on antibiotics by 1.66% (p < 0.01), followed by a sustained reduction of 3.12% at 18 months (p < 0.01). Piperacillin-tazobactam appropriateness increased by 2.76% at 1 month (p = 0.11) and further increased by 7.09% at 18 months (p < 0.05). Similarly, ceftriaxone appropriateness increased by 5.74% (p < 0.05) at 1 month and remained above pre-ABxSG levels. There was a significant increase in the number of pharmacist-led interventions for dosing optimization, with 96 more interventions/month at 18 months (p = 0.14). Conclusion: Antimicrobial stewardship teams must remain agile, embrace innovations, and adopt digital technologies to engage and empower clinicians. ABxSG reduced the proportion of inpatients on antibiotics and improved antibiotic prescriptions. The incorporation of BDTA in ABxSG, strong hospital leader support, and strategic planning to promote adoption led to its success. Full article

While autonomous vehicles (AVs) are increasingly integrated into urban mobility, little is known about how digital capability shapes elderly pedestrians’ willingness to share roads with these technologies. This is especially true in the absence of explicit vehicle–pedestrian communication mechanisms. To address this gap, we combine the Theory of Planned Behavior (TPB) with the Pedestrian Behavior Questionnaire (PBQ) and segment elderly pedestrians using Latent Class Analysis (LCA). A sample of 750 older adults in Wuhan, China, was divided into two latent groups: digitally disengaged (70.8%) and digitally engaged (29.2%). Classification was based on four indicators: smart device usage, online social interaction, online entertainment, and online economic behavior. We then applied ordered logit models to estimate group-specific determinants of AV road-sharing acceptance. Results reveal clear heterogeneity across digital capability levels. For digitally disengaged seniors, positive pedestrian behaviors significantly increased willingness (β = 0.316, p = 0.001). Prior accident experience reduced willingness (0 accident: β = 0.435, p = 0.021; 1–2 accidents: β = −0.518, p = 0.012). For digitally engaged seniors, perceived behavioral control showed a marginally positive effect (β = 0.353, p = 0.066). Errors had a significant positive effect (β = 0.540, p = 0.009). Positive behaviors had a significant negative effect (β = −0.414, p = 0.007). These patterns indicate that digital capability not only modulates the strength of TPB pathways but also reshapes behavior–intention linkages captured by PBQ dimensions. Methodologically, the study contributes an integrated TPB–PBQ–LCA–OLM framework. This framework identifies digital capability as a critical moderator of AV acceptance among elderly pedestrians. Practically, the findings suggest differentiated strategies. For digitally disengaged users, interventions should build digital literacy and reinforce safe walking norms. For digitally engaged users, strategies should prioritize transparent AV intent signaling and features that enhance perceived control. Full article