Search Results (107)

Introduction: Step counts are increasingly used to assess mobility and track recovery following total knee arthroplasty (TKA). The purpose of this study was to assess the convergent validity of step count data captured by a smart implantable device (SID) in comparison with step counts derived from established, validated sensor-based technology. Methods: A secondary analysis of an anonymized commercial database (N = 7861, median age: 68, female: 59%, median BMI: 31.7) of patients who received an SID and used a digital care management application (App) with or without a smart watch. The SID recorded “qualified steps”, defined as periods of walking for at least seven steps that met predefined acceleration and cadence thresholds between 7 am and 10 pm. The App collected total daily step counts via smartwatch and/or smartphone. Pearson correlations were calculated between SID and App data at 30, 90, and 180 days post-operative. Step counts at 30, 90, and 180 days post-operative were compared between groups with the Mann–Whitney U test. Statistical significance was assessed at p < 0.001. Results: Step counts increased throughout the recovery period as measured by all three devices. SID-captured fewer qualified steps than App-captured step counts from watch-wearers throughout the post-operative period (p ≤ 0.001). SID step counts were similar to App step counts at 30 days post-operative and greater than App step counts at 90 and 180 days post-operative (p < 0.001). There were significant (p < 0.001), moderate correlations (r = 0.62 to r = 0.74) between step counts collected by the SID and App for both watch-wearers and smartphone-carriers at 30, 90, and 180 days post-operative. Conclusions: The SID’s qualified step metric demonstrated consistent, moderate, correlations with app-based step counts across 30, 90, and 180 days. While smartwatch-based tools recorded higher absolute step counts, both technologies reflected similar recovery trajectories. Full article

This paper presents a repeated cross-sectional longitudinal (trend) analysis of students’ self-perceived sustainability competence development across three waves surrounding participation in the Art Nouveau Path, a heritage-based mobile augmented reality game designed to foster sustainability competences, located in Aveiro, Portugal. In total, 1094 questionnaires were collected using a GreenComp-grounded instrument adapted from the GreenComp-based Questionnaire (GCQuest) to this context (25 items; 6-point Likert). Data were gathered at three stages: pre-intervention (S1-PRE; N = 221), immediately post-intervention (S2-POST; N = 439; n = 438 retained for scale scoring after applying a predefined completeness criterion), and follow-up (S3-FU; N = 434). Because responses were anonymous, waves were treated as independent samples rather than within-student trajectories. The Embodying Sustainability Values domain score and item-level response distributions were compared across waves using ordinal-appropriate non-parametric group comparisons, effect-size estimation, and descriptive threshold indicators. Results indicate an improvement from pre-intervention to post-intervention, followed by partial attenuation at follow-up while remaining above pre-intervention. Mean scores increased from 3.70 (S1-PRE) to 4.64 (S2-POST) and then stabilized at 4.13 (S3-FU). Findings, while exploratory, suggest that this heritage-based augmented reality game may have enhanced perceived sustainability competences. A structured program of follow-up activities is proposed to help sustain gains. Full article

The World Health Organization’s Bangkok Declaration, ‘No health without oral health,’ recognizes oral health as a global public health priority. Despite being largely preventable, oral diseases affect nearly half of the global population, and India mirrors this crisis while facing persistent systemic challenges. This qualitative study explores India’s oral healthcare landscape from the perspective of public health dentists to inform context-sensitive reforms. Thirty-one in-depth interviews were conducted with public health dentists from dental colleges registered with the Dental Council of India, recruited across six regions. Interviews were conducted online via MS Teams using a piloted interview guide and video-recorded with consent. Subsequently, the interviews were transcribed verbatim, anonymized, and qualitative data was analyzed using Atlas.ti, following reflexive thematic analysis. Analysis yielded four main themes: facets of oral health inequalities, dental public health initiatives, strategies to mobilize and optimize dental workforce in rural areas, and recommendations to optimize oral healthcare. This study offers contextually grounded yet globally relevant perspectives on oral health reform. By bridging local insights with international priorities, this study proposes a sustainable, equity-driven framework for transforming oral health systems while laying the foundation for future research and policy action aimed at achieving universal oral health coverage. Full article

Location-based services (LBSs), which are used for navigation, tracking, and mapping across digital devices and social platforms, establish a user’s position and deliver tailored experiences. Collecting and sharing such trajectory datasets with analysts for business purposes raises critical privacy concerns, as both symmetry in recurring behavior mobility patterns and asymmetry in irregular movement mobility patterns in sensitive locations collectively expose highly identifiable information, resulting in re-identification risks, trajectory disclosure, and location inference. In response, several privacy preservation models have been proposed, including k-anonymity, l-diversity, t-closeness, LKC-privacy, differential privacy, and location-based approaches. However, these models still exhibit privacy issues, including sensitive location inference (e.g., hospitals, pawnshops, prisons, safe houses), disclosure from duplicate trajectories revealing sensitive places, and the re-identification of unique locations such as homes, condominiums, and offices. Efforts to address these issues often lead to utility loss and computational complexity. To overcome these limitations, we propose a new ($\xi$, $ϵ$)-privacy model that combines data generalization and suppression with sliding windows and R-Tree structures, where sliding windows partition large trajectory graphs into simplified subgraphs, R-Trees provide hierarchical indexing for spatial generalization, and suppression removes highly identifiable locations. The model addresses both symmetry and asymmetry in mobility patterns by balancing generalization and suppression to protect privacy while maintaining data utility. Symmetry-driven mechanisms that enhance resistance to inference attacks and support data confidentiality, integrity, and availability are core requirements of cryptography and information security. An experimental evaluation on the City80k and Metro100k datasets confirms that the ($\xi$, $ϵ$)-privacy model addresses privacy issues with reduced utility loss and efficient scalability, while validating robustness through relative error across query types in diverse analytical scenarios. The findings provide evidence of the model’s practicality for large-scale location data, confirming its relevance to secure computation, data protection, and information security applications. Full article

The growing complexity of urban mobility necessitates more adaptive, data-driven approaches to transport demand forecasting. This study incorporates anonymized Call Detail Record (CDR) data—originally collected for mobile network billing—into the conventional four-step travel demand model to more accurately estimate trip behavior. Employing a stay-based method, significant user locations are identified, and individual mobility patterns are reconstructed. These patterns are then aggregated at the zonal level and validated against a large-scale household survey conducted in Sri Lanka. The proposed framework enables the extraction of origin–destination matrices and supports route assignment using CDR data, demonstrating a strong correlation with traditional survey results. This research highlights the potential of repurposed CDR data as a scalable, cost-efficient alternative to conventional travel surveys for estimating travel demand. Full article

Background: The longitudinal RisCoin study investigated risk factors for COVID-19 vaccination failure among healthcare workers (HCWs) and patients with inflammatory bowel disease (IBD) at a University Hospital in Germany. Since the hospital served as the study sponsor and employer of the HCW, we implemented a custom mobile application. We aimed to evaluate the implementation, adherence, benefits, and limitations of this study’s app. Methods: The app allowed secure data collection through questionnaires, disseminated serological results, and managed bidirectional communication. Access was double-pseudonymized and irreversibly anonymized six months after enrollment. Download frequency, login events, and questionnaire submissions between October 2021 and December 2022 were analyzed. Multivariable logistic regression identified factors associated with app adherence. Results: Of the 3979 participants with app access, 3622 (91%) used the app; out of these, 1016 (28%) were “adherent users” (≥12 submitted questionnaires). App adherence significantly increased with age. Among HCW, adherent users were more likely to be non-smokers (p < 0.001), working as administrators or nursing staff vs. physicians (p < 0.001), vaccinated against influenza (p < 0.001), and had not travelled abroad in the past year (p < 0.001). IBD patients exposed to SARS-CoV-2 (p = 0.0133) and those with adverse events following the second COVID-19 vaccination (p = 0.0171) were more likely adherent app users. Despite technical issues causing dropout or non-adherence, the app served as a secure solution for cohort management and longitudinal data collection. Discussion: App-based cohort management enabled continuous data acquisition and individualized care while providing flexibility and anonymity for the study team and participants. App usability, technical issues, and cohort characteristics need to be thoroughly considered prior to implementation to optimize usage and adherence in clinical research. Full article

Mobility data plays a crucial role in modern location-based services (LBSs), yet it poses significant privacy risks, as it can reveal highly sensitive information such as home locations and behavioral patterns. This paper focuses on the anonymization of mobility data by obfuscating mobility heat maps and combining this with a local differential privacy method, which generates synthetic mobility traces. Using the San Francisco Cabspotting dataset, we compare the effectiveness of the combined approach against reidentification attacks. Our results show that mobility traces treated with both a heat map obfuscation and local differential privacy are less likely to be reidentified than those anonymized solely with Heat Map Confusion. This two-tiered anonymization process balances the trade-off between privacy and data utility, providing a robust defense against reidentification while preserving data accuracy for practical applications. The findings suggest that the integration of synthetic trace generation with heat map-based obfuscation can significantly enhance the protection of mobility data, offering a stronger solution for privacy-preserving data sharing. Full article

This study explores the potential of mobile augmented reality games (MARGs) in promoting sustainability competencies within the context of a smart learning city environment. Anchored in the EduCITY project, which integrates location-based AR-enhanced games into an interactive mobile app, the research investigates how these tools support Education for Sustainable Development (ESD). Employing a mixed-methods approach, data were collected through the GreenComp-based Questionnaire (GCQuest) and anonymous gameplay logs generated by the app. Thematic analysis of 358 responses revealed four key learning domains: ‘cultural awareness’, ‘environmental protection’, ‘sustainability awareness’, and ‘contextual knowledge’. Quantitative performance data from game logs highlighted substantial variation across games, with the highest performance found in those with more frequent AR integration and multiple iterative refinements. Participants engaging with AR-enhanced features (optional) outperformed others. This study provides empirical evidence for the use of MARGs to cultivate sustainability-related knowledge, skills, and attitudes, particularly when grounded in local realities and enhanced through thoughtful design. Beyond the EduCITY project, the study proposes a replicable model for assessing sustainability competencies, with implications for broader integration of AR across educational contexts in ESD. The paper concludes with a critical reflection on methodological limitations and suggests future directions, including adapting the GCQuest for use with younger learners in primary education. Full article

The COVID-19 pandemic has profoundly influenced urban planning and disease management in residential areas. Focusing on Beijing as a case study (3898 communities), this research develops a big data analytics framework integrating anonymized mobile phone signals (China Mobile), location-based services (AMAP.com), and municipal health records to quantify COVID-19 transmission dynamics. Using logistic regression, we analyzed 15 indicators across four dimensions: mobility behavior, host demographics, spatial characteristics, and facility accessibility. Our analysis reveals three key determinants: (1) Population aged 65 and above (OR = 62.8, p < 0.001) and (2) housing density (OR = 9.96, p = 0.026) significantly increase transmission risk, while (3) population density exhibits a paradoxical negative effect (β = −3.98, p < 0.001) attributable to targeted interventions in high-density zones. We further construct a validated risk prediction model (AUC = 0.7; 95.97% accuracy) enabling high-resolution spatial targeting of non-pharmaceutical interventions (NPIs). The framework provides urban planners with actionable strategies—including senior activity scheduling and ventilation retrofits—while advancing scalable methodologies for infectious disease management in global urban contexts. Full article

With the growing integration of the Internet of Things (IoT), low-altitude intelligent networks, and vehicular networks, smart city traffic systems are gradually evolving into an air–ground integrated intelligent monitoring framework. However, traditional centralized model training faces challenges such as high network load due to massive data transmission, energy management difficulties for mobile devices like UAVs, and privacy risks associated with non-anonymized road operation data. Therefore, this paper proposes an air–ground collaborative federated learning framework that integrates Bayesian prediction and an incentive mechanism to achieve privacy protection and communication optimization through localized model training and differentiated incentive strategies. Simulation experiments demonstrate that, compared to the Equal Contribution Algorithm (ECA) and the Importance Contribution Algorithm (ICA), the proposed method improves model convergence speed while reducing incentive costs, providing theoretical support for the reliable operation of large-scale intelligent traffic monitoring systems. Full article

Precisely estimating a child’s body measurements and weight from a single image is useful in pediatrics for monitoring growth and detecting early signs of malnutrition. The development of estimation models for this task is hampered by the unavailability of a labeled image dataset to support supervised learning. This paper introduces the “Age-Restricted Anonymized” (ARAN) dataset, the first labeled image dataset of children with body measurements approved by an ethics committee under the European General Data Protection Regulation guidelines. The ARAN dataset consists of images of 512 children aged 16 to 98 months, each captured from four different viewpoints, i.e., 2048 images in total. The dataset is anonymized manually on the spot through a face mask and includes each child’s height, weight, age, waist circumference, and head circumference measurements. The dataset is a solid foundation for developing prediction models for various tasks related to these measurements; it addresses the gap in computer vision tasks related to body measurements as it is significantly larger than any other comparable dataset of children, along with diverse viewpoints. To create a suitable reference, we trained state-of-the-art deep learning algorithms on the ARAN dataset to predict body measurements from the images. The best results are obtained by a DenseNet121 model achieving competitive estimates for the body measurements, outperforming state-of-the-art results on similar tasks. The ARAN dataset is developed as part of a collaboration to create a mobile app to measure children’s growth and detect early signs of malnutrition, contributing to the United Nations Sustainable Development Goals. Full article

Vacuum-assisted and forceps-assisted vaginal births are associated with higher rates of formula supplementation and shorter breastfeeding duration compared to unassisted vaginal births; however, the reasons for this are unclear. Factors such as maternal knowledge, partner support, and parity significantly influence breastfeeding initiation and duration. The prevalence of perineal trauma, neonatal and maternal birth complications, and decreased birth satisfaction is higher after assisted births and may also impact breastfeeding outcomes. Given the limited research on the specific effects of different vaginal birth modes on breastfeeding, this study aimed to examine women’s experiences of establishing breastfeeding after unassisted, vacuum-assisted, and forceps-assisted vaginal birth. A mixed-methods study design was employed using an anonymous online questionnaire, which included binary, multiple choice, and open-ended questions, and Likert scale items. Using social media, we recruited Australian women who had an unassisted, vacuum-assisted, or forceps-assisted birth within the last year. Details of participant demographics, breastfeeding history, initiation and establishment, postpartum mobility, and pain ratings were recorded. Additionally, qualitative data on postpartum recovery and breastfeeding support were analysed using an inductive thematic analysis framework. A total of 565 women were recruited between May and June 2024, of which 488 responses were retained for analysis. Thematic analysis of the qualitative responses identified four central themes that defined women’s experiences of establishing breastfeeding and were similar between unassisted or assisted vaginal birth modes: Experience of Care, Environment, Expectations, and Health Complications. A range of both positive and negative experiences of breastfeeding support, environmental factors, and expectations of the realities of breastfeeding impacted women’s experiences. For many women, various maternal and/or newborn health issues, nipple pain, and latching difficulties made breastfeeding more difficult. Commercial milk formula supplementation during the hospital stay was more prevalent after a forceps-assisted birth when compared to unassisted vaginal birth (41% vs. 17%, respectively; p < 0.001). Further, during the first two weeks at home, commercial milk formula supplementation was more prevalent after both forceps-assisted (26%) and vacuum-assisted (23%) births than after unassisted vaginal birth (8%, p < 0.001). Pain ratings in the early days following birth and in the first two weeks at home were significantly higher for the forceps-assisted group than for the other vaginal birth modes (p ≤ 0.005). Women that had an unassisted vaginal birth with an intact perineum had the lowest pain ratings in the early days and weeks after birth, while pain ratings were similar between women that had a vacuum-assisted birth and those who had an unassisted vaginal birth with a perineal tear or episiotomy (p = 0.05). Early commercial milk formula supplementation is associated with shorter breastfeeding duration, while postpartum pain is known to impede maternal mobility and may partially inhibit the milk ejection reflex, potentially negatively impacting breastfeeding and increasing formula use. Therefore, women who have an instrumental assisted vaginal birth, particularly those who have a forceps-assisted birth, are at greater risk of suboptimal breastfeeding outcomes including short durations of exclusive and any breastfeeding. Improvements to early postpartum pain management, breastfeeding education, and the judicious use of commercial milk formula may improve breastfeeding and subsequent maternal and health outcomes after instrument-assisted vaginal birth. Full article

Veterinary anaesthesia requires theoretical knowledge and quick decision-making skills. Traditional education may not adequately prepare students, while simulation-based learning enhances engagement and skill development. This study evaluates the effectiveness of a Twine-based web system in improving experiential learning, engagement, knowledge retention, and decision-making skills in veterinary anaesthesia students. Five interactive clinical cases were developed using Twine, simulating realistic anaesthesia scenarios with decision points and gamified elements, such as scoring systems and resource management. These modules were accessible on various devices via the web. Following a workshop for second- to fourth-year students of the Degree in Veterinary Sciences, an anonymous survey assessed the module’s effectiveness. Quantitative data were analysed descriptively, while qualitative feedback was processed through a hybrid AI–human thematic analysis. Out of 849 invited students, 367 responded (42% response rate). Feedback was highly positive; 90.8% found it effective for training, and 97.0% agreed it improved knowledge. User-friendliness was rated as “easy” or “very easy” by 94.6%. Regarding overall satisfaction, 96.7% of students described the workshop as “good” or “excellent”. Some participants suggested improvements in mobile device compatibility and the need for additional resources to understand the concepts better. Twine’s interactive format fosters experiential learning while reducing reliance on live animals, aligning with modern ethical standards. Its accessibility via web and translation-enabled browsers enhances its reach. Future research should examine Twine’s impact on clinical skills retention and adaptability in various educational contexts, providing a flexible approach to veterinary anaesthesia education through gamified learning. Full article

This paper addresses the challenge of preserving user privacy in location-based services (LBSs) by proposing a novel, complementary approach to existing privacy-preserving techniques such as k-anonymity and l-diversity. Our approach implements collaborative caching strategies within a mobile ad hoc network (MANET), exploiting the geographic of location-based queries (LBQs) to reduce data exposure to untrusted LBS servers. Unlike existing approaches that rely on centralized servers or stationary infrastructure, our solution facilitates direct data exchange between users’ devices, providing an additional layer of privacy protection. We introduce a new privacy entropy-based metric called accumulated privacy loss (APL) to quantify the privacy loss incurred when accessing either the LBS or our proposed system. Our approach implements a two-tier caching strategy: local caching maintained by each user and neighbor caching based on proximity. This strategy not only reduces the number of queries to the LBS server but also significantly enhances user privacy by minimizing the exposure of location data to centralized entities. Empirical results demonstrate that while our collaborative caching system incurs some communication costs, it significantly mitigates redundant data among user caches and reduces the need to access potentially privacy-compromising LBS servers. Our findings show a 40% reduction in LBS queries, a 64% decrease in data redundancy within cells, and a 31% reduction in accumulated privacy loss compared to baseline methods. In addition, we analyze the impact of data obsolescence on cache performance and privacy loss, proposing mechanisms for maintaining the relevance and accuracy of cached data. This work contributes to the field of privacy-preserving LBSs by providing a decentralized, user-centric approach that improves both cache redundancy and privacy protection, particularly in scenarios where central infrastructure is unreachable or untrusted. Full article

The increasing use of mobile networks is an opportunity to collect and model users’ movement data for extracting knowledge about life and health while considering privacy leakage risk. This study aims to approximate the lifestyles of urban residents, employing statistical information derived from their movements among various Points of Interest (PoI). Our investigations comprehend a multidimensional analysis of key urban factors to provide insights into the population’s daily routines, preferences, and characteristics. To this end, we developed a framework called LEAF that models lifestyles by interpreting anonymized cell phone mobility data and integrating it with information from other sources, such as geographical layers of land use and sets of PoI. LEAF presents the information in a vector space model capable of responding to spatial queries about lifestyle. We also developed a consolidated lifestyle pattern framework to systematically identify and analyze the dominant activity patterns in different urban areas. To evaluate the effectiveness of the proposed framework, we tested it on movement data from individuals in a medium-sized city and compared the results with information collected through surveys. The RMSE of $5.167$ between the proposed framework’s results and survey-based data indicates that the framework provides a reliable estimation of lifestyle patterns across diverse urban areas. Additionally, summarized patterns of criteria ordering were created, offering a concise and intuitive representation of lifestyles. The analysis revealed high consistency between the two methods in the derived patterns, underscoring the framework’s robustness and accuracy in modeling urban lifestyle dynamics. Full article