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Existing research fails to effectively address the problem of increased GNSS positioning errors caused by non-line-of-sight (NLOS) and line-of-sight (LOS) signal attenuation due to obstructions such as buildings and trees in complex urban environments. To address this issue, we dig into the environmental perception perspective to propose a semantic segmentation-based GNSS signal occlusion detection and optimization method. The approach distinguishes between building and tree occlusions and adjusts signal weights accordingly to enhance positioning accuracy. First, a fisheye camera captures environmental imagery above the vehicle, which is then processed using deep learning to segment sky, tree, and building regions. Subsequently, satellite projections are mapped onto the segmented sky image to classify signal occlusions. Then, based on the type of obstruction, a dynamic weight optimization model is constructed to adjust the contribution of each satellite in the positioning solution, thereby enhancing the positioning accuracy of vehicle-navigation in urban environments. Finally, we construct a vehicle-mounted navigation system for experimentation. The experimental results demonstrate that the proposed method enhances accuracy by 16% and 10% compared to the existing GNSS/INS/Canny and GNSS/INS/Flood Fill methods, respectively, confirming its effectiveness in complex urban environments. Full article

(This article belongs to the Special Issue GNSS and Multi-Sensor Integrated Precise Positioning and Applications)

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23 pages, 1029 KiB

Open AccessArticle

Lattice-Based Certificateless Proxy Re-Signature for IoT: A Computation-and-Storage Optimized Post-Quantum Scheme

by Zhanzhen Wei, Gongjian Lan, Hong Zhao, Zhaobin Li and Zheng Ju

Sensors 2025, 25(15), 4848; https://doi.org/10.3390/s25154848 (registering DOI) - 6 Aug 2025

Abstract

Proxy re-signature enables transitive authentication of digital identities across different domains and has significant application value in areas such as digital rights management, cross-domain certificate validation, and distributed system access control. However, most existing proxy re-signature schemes, which are predominantly based on traditional public-key cryptosystems, face security vulnerabilities and certificate management bottlenecks. While identity-based schemes alleviate some issues, they introduce key escrow concerns. Certificateless schemes effectively resolve both certificate management and key escrow problems but remain vulnerable to quantum computing threats. To address these limitations, this paper constructs an efficient post-quantum certificateless proxy re-signature scheme based on algebraic lattices. Building upon algebraic lattice theory and leveraging the Dilithium algorithm, our scheme innovatively employs a lattice basis reduction-assisted parameter selection strategy to mitigate the potential algebraic attack vectors inherent in the NTRU lattice structure. This ensures the security and integrity of multi-party communication in quantum-threat environments. Furthermore, the scheme significantly reduces computational overhead and optimizes signature storage complexity through structured compression techniques, facilitating deployment on resource-constrained devices like Internet of Things (IoT) terminals. We formally prove the unforgeability of the scheme under the adaptive chosen-message attack model, with its security reducible to the hardness of the corresponding underlying lattice problems. Full article

(This article belongs to the Special Issue IoT Network Security (Second Edition))

40 pages, 2515 KiB

Open AccessArticle

AE-DTNN: Autoencoder–Dense–Transformer Neural Network Model for Efficient Anomaly-Based Intrusion Detection Systems

by Hesham Kamal and Maggie Mashaly

Mach. Learn. Knowl. Extr. 2025, 7(3), 78; https://doi.org/10.3390/make7030078 - 6 Aug 2025

Abstract

In this study, we introduce an enhanced hybrid Autoencoder–Dense–Transformer Neural Network (AE-DTNN) model for developing an effective intrusion detection system (IDS) aimed at improving the performance and robustness of threat detection strategies within a rapidly changing and increasingly complex network landscape. The Autoencoder component restructures network traffic data, while a stack of Dense layers performs feature extraction to generate more meaningful representations. The Transformer network then facilitates highly precise and comprehensive classification. Our strategy incorporates adaptive synthetic sampling (ADASYN) for both binary and multi-class classification tasks, complemented by the edited nearest neighbors (ENN) technique and the use of class weights to mitigate class imbalance issues. In experiments conducted on the NF-BoT-IoT-v2 dataset, the AE-DTNN-based IDS achieved outstanding performance, with 99.98% accuracy in binary classification and 98.30% in multi-class classification. On the NSL-KDD dataset, the model reached 98.57% accuracy for binary classification and 97.50% for multi-class classification. Additionally, the model attained 99.92% and 99.78% accuracy in binary and multi-class classification, respectively, on the CSE-CIC-IDS2018 dataset. These results demonstrate the exceptional effectiveness of the proposed model in contrast to conventional approaches, highlighting its strong potential to detect a broad range of network intrusions with high reliability. Full article

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30 pages, 22926 KiB

Open AccessArticle

Comparative Study to Evaluate Mixing Efficiency of Very Fine Particles

by Sung Je Lee and Se-Yun Hwang

Appl. Sci. 2025, 15(15), 8712; https://doi.org/10.3390/app15158712 (registering DOI) - 6 Aug 2025

Abstract

This study evaluates the applicability and accuracy of coarse-grain modeling (CGM) in discrete-element method (DEM)–based simulations, focusing on particle-mixing efficiency in five representative industrial mixers: the tumbling V mixer, ribbon-blade mixer, paddle-blade mixer, vertical-blade mixer, and conical-screw mixer. Although the DEM is widely employed for particulate system simulations, the high computational cost associated with fine particles significantly hinders large-scale applications. CGM addresses these issues by scaling up particle sizes, thereby reducing particle counts and allowing longer simulation timesteps. We utilized the Lacey mixing index (LMI) as a statistical measure to quantitatively assess mixing uniformity across various CGM scaling factors. Based on the results, CGM significantly reduced computational time (by over 90% in certain cases) while preserving acceptable accuracy levels in terms of LMI values. The mixing behaviors remained consistent under various CGM conditions, based on both visually inspected particle distributions and the temporal LMI trends. Although minor deviations occurred in early-stage mixing, these discrepancies diminished with time, with the final LMI errors remaining below 5% in most scenarios. These findings indicate that CGM effectively enhances computational efficiency in DEM simulations without significantly compromising physical accuracy. This research provides practical guidelines for optimizing industrial-scale particle-mixing processes and conducting computationally feasible, scalable, and reliable DEM simulations. Full article

(This article belongs to the Special Issue Advances and Applications of CFD (Computational Fluid Dynamics), 2nd Edition)

26 pages, 6895 KiB

Open AccessArticle

Generation of Individualized, Standardized, and Electrically Synchronized Human Midbrain Organoids

by Sanae El Harane, Bahareh Nazari, Nadia El Harane, Manon Locatelli, Bochra Zidi, Stéphane Durual, Abderrahim Karmime, Florence Ravier, Adrien Roux, Luc Stoppini, Olivier Preynat-Seauve and Karl-Heinz Krause

Cells 2025, 14(15), 1211; https://doi.org/10.3390/cells14151211 - 6 Aug 2025

Abstract

Organoids allow to model healthy and diseased human tissues. and have applications in developmental biology, drug discovery, and cell therapy. Traditionally cultured in immersion/suspension, organoids face issues like lack of standardization, fusion, hypoxia-induced necrosis, continuous agitation, and high media volume requirements. To address these issues, we developed an air–liquid interface (ALi) technology for culturing organoids, termed AirLiwell. It uses non-adhesive microwells for generating and maintaining individualized organoids on an air–liquid interface. This method ensures high standardization, prevents organoid fusion, eliminates the need for agitation, simplifies media changes, reduces media volume, and is compatible with Good Manufacturing Practices. We compared the ALi method to standard immersion culture for midbrain organoids, detailing the process from human pluripotent stem cell (hPSC) culture to organoid maturation and analysis. Air–liquid interface organoids (3D-ALi) showed optimized size and shape standardization. RNA sequencing and immunostaining confirmed neural/dopaminergic specification. Single-cell RNA sequencing revealed that immersion organoids (3D-i) contained 16% fibroblast-like, 23% myeloid-like, and 61% neural cells (49% neurons), whereas 3D-ALi organoids comprised 99% neural cells (86% neurons). Functionally, 3D-ALi organoids showed a striking electrophysiological synchronization, unlike the heterogeneous activity of 3D-i organoids. This standardized organoid platform improves reproducibility and scalability, demonstrated here with midbrain organoids. The use of midbrain organoids is particularly relevant for neuroscience and neurodegenerative diseases, such as Parkinson’s disease, due to their high incidence, opening new perspectives in disease modeling and cell therapy. In addition to hPSC-derived organoids, the method’s versatility extends to cancer organoids and 3D cultures from primary human cells. Full article

(This article belongs to the Special Issue The Current Applications and Potential of Stem Cell-Derived Organoids)

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17 pages, 3157 KiB

Open AccessArticle

Research on Online Traceability Methods for the Causes of Longitudinal Surface Crack in Continuous Casting Slab

by Junqiang Cong, Qiancheng Lv, Zihao Fan, Haitao Ling and Fei He

Materials 2025, 18(15), 3695; https://doi.org/10.3390/ma18153695 - 6 Aug 2025

Abstract

In the casting and rolling production process, surface longitudinal cracks are a typical casting defect. Tracing the causes of longitudinal cracks online and controlling the key parameters leading to their formation in a timely manner can enhance the stability of casting and rolling production. To this end, the influencing factors of longitudinal cracks were analyzed, a data integration storage platform was constructed, and a tracing model was established using empirical rule analysis, statistical analysis, and intelligent analysis methods. During the initial production phase of a casting machine, longitudinal cracks occurred frequently. The tracing results using the LightGBM-SHAP method showed that the relative influence of the narrow left wide inner heat flow ratio of the mold was significant, followed by the heat flow difference on the wide symmetrical face of the mold and the superheat of the molten steel, with weights of 0.135, 0.066, and 0.048, respectively. Based on the tracing results, we implemented online emergency measures. By controlling the cooling intensity of the mold, we effectively reduced the recurrence rate of longitudinal cracks. Root cause analysis revealed that the total hardness of the mold-cooling water exceeded the standard, reaching 24 mg/L, which caused scaling on the mold copper plates and uneven cooling, leading to the frequent occurrence of longitudinal cracks. After strictly controlling the water quality, the issue of longitudinal cracks was brought under control. The online application of the tracing method for the causes of longitudinal cracks has effectively improved efficiency in resolving longitudinal crack problems. Full article

(This article belongs to the Special Issue Advanced Sheet/Bulk Metal Forming)

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53 pages, 3243 KiB

Open AccessReview

Shaping Sustainability Through Food Consumption: A Conceptual Perspective

by Juta Deksne, Jelena Lonska, Lienite Litavniece and Tatjana Tambovceva

Sustainability 2025, 17(15), 7138; https://doi.org/10.3390/su17157138 - 6 Aug 2025

Abstract

The food consumption stage, the final step in the food supply chain (FSC), where food has already undergone resource-intensive processes, plays a central role in the transition to a sustainable food system. Consumers’ food choices and consumption practices directly influence food demand, production methods, and resource use across the FSC. These factors affect global challenges such as overconsumption, malnutrition, hunger, and food waste (FW)—issues integral to the UN Sustainable Development Goals (SDGs). Therefore, this study aims to identify key aspects of the food consumption stage that influence the shift toward sustainability and to develop a conceptual framework to guide this transition. To achieve this, an integrative literature review (ILR), supported by bibliometric analysis and narrative review elements, was conducted to strengthen the conceptual foundation. The results reveal four central aspects: FW and its reduction, the need for dietary shifts, changes in consumer behaviour, and policy reform, highlighting the consumer and their behaviour as the central connecting element. Based on the findings, a framework was developed linking the identified problems with targeted solutions, which can be implemented through various tools that also act as drivers of change, enhancing sustainable food consumption, food system sustainability, and the achievement of global SDGs. Full article

17 pages, 962 KiB

Open AccessArticle

Impact of COVID-19 on Mental Health in Nursing Students and Non-Nursing Students: A Cross-Sectional Study

by Verena Dresen, Liliane Sigmund, Siegmund Staggl, Bernhard Holzner, Gerhard Rumpold, Laura R. Fischer-Jbali, Markus Canazei and Elisabeth Weiss

Nurs. Rep. 2025, 15(8), 286; https://doi.org/10.3390/nursrep15080286 - 6 Aug 2025

Abstract

Background/Objective: Nursing and non-nursing students experience high stress levels, making them susceptible to mental health issues. This study compared stress, anxiety, and depression between these two groups after 2 years of the COVID-19 pandemic. Additionally, it explored the relationship between perceived helplessness, self-efficacy, and symptoms of mental stress and strain resulting from challenging internship conditions for nursing students. Methods: This cross-sectional study included 154 nursing students (mean age = 22.43 years) and 291 non-nursing students (mean age = 27.7 years). Data were collected using the Depression Anxiety Stress Scales (DASS-21), Perceived Stress Scale-10 (PSS-10), and a questionnaire on mental stress and strain. Results: Nursing students reported significantly higher scores in the DASS-21 subscales depression (ηp² = 0.016) and anxiety (ηp² = 0.037), and global stress (PSS-10; ηp² = 0.029) compared to non-nursing students, but no significant difference on the DASS-21 Stress subscale. The observed group differences in the present study may be partially attributed to group differences in demographic factors. Helplessness correlated strongly with nearly all scales of mental stress and strain during internships (all p’s < 0.001), while self-efficacy showed a strong negative correlation with non-occupational difficulties, health impairment, and emotional problems (all p’s < 0.001). Conclusions: Nursing students experience elevated depression, anxiety, and perceived stress levels compared to non-nursing students. Stronger feelings of helplessness and lower confidence in their ability to overcome challenges were strongly correlated with mental stress and strain during clinical training. Targeted interventions such as cognitive behavioral training and stress management should be integrated into nursing curricula to enhance resilience and coping strategies. Full article

(This article belongs to the Special Issue Mental Health and Well-Being of Nursing Students: Challenges, Interventions, and Future Directions)

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14 pages, 1215 KiB

Open AccessArticle

Daptomycin-Loaded Nano-Drug Delivery System Based on Biomimetic Cell Membrane Coating Technology: Preparation, Characterization, and Evaluation

by Yuqin Zhou, Shihan Du, Kailun He, Beilei Zhou, Zixuan Chen, Cheng Zheng, Minghao Zhou, Jue Li, Yue Chen, Hu Zhang, Hong Yuan, Yinghong Li, Yan Chen and Fuqiang Hu

Pharmaceuticals 2025, 18(8), 1169; https://doi.org/10.3390/ph18081169 - 6 Aug 2025

Abstract

Background/Objective: Staphylococcus aureus (S. aureus) is a clinically significant pathogenic bacterium. Daptomycin (DAP) is a cyclic lipopeptide antibiotic used to treat infections caused by multidrug-resistant Gram-positive bacteria, including S. aureus. However, DAP currently faces clinical limitations due to its short half-life, toxic side effects, and increasingly severe drug resistance issues. This study aimed to develop a biomimetic nano-drug delivery system to enhance targeting ability, prolong blood circulation, and mitigate resistance of DAP. Methods: DAP-loaded chitosan nanocomposite particles (DAP-CS) were prepared by electrostatic self-assembly. Macrophage membrane vesicles (MM) were prepared by fusion of M1-type macrophage membranes with 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC). A biomimetic nano-drug delivery system (DAP-CS@MM) was constructed by the coextrusion process of DAP-CS and MM. Key physicochemical parameters, including particle diameter, zeta potential, encapsulation efficiency, and membrane protein retention, were systematically characterized. In vitro immune escape studies and in vivo zebrafish infection models were employed to assess the ability of immune escape and antibacterial performance, respectively. Results: The particle size of DAP-CS@MM was 110.9 ± 13.72 nm, with zeta potential +11.90 ± 1.90 mV, and encapsulation efficiency 70.43 ± 1.29%. DAP-CS@MM retained macrophage membrane proteins, including functional TLR2 receptors. In vitro immune escape assays, DAP-CS@MM demonstrated significantly enhanced immune escape compared with DAP-CS (p < 0.05). In the zebrafish infection model, DAP-CS@MM showed superior antibacterial efficacy over both DAP and DAP-CS (p < 0.05). Conclusions: The DAP-CS@MM biomimetic nano-drug delivery system exhibits excellent immune evasion and antibacterial performance, offering a novel strategy to overcome the clinical limitations of DAP. Full article

(This article belongs to the Section Pharmaceutical Technology)

20 pages, 321 KiB

Open AccessArticle

The Swedish Adoption World and the Process of Coming to Terms with Transnational Adoption

by Tobias Hübinette

Genealogy 2025, 9(3), 77; https://doi.org/10.3390/genealogy9030077 - 6 Aug 2025

Abstract

In October 2021 the Swedish government committee of inquiry, the Adoption Commission, was appointed, which presented its final report in June 2025. The Adoption Commission investigated irregular and unethical adoptions to Sweden from the 1950s until today, and it was a part of an ongoing global process of coming to terms with past concerning transnational adoptions. This qualitative media text study examines how the Adoption Commission was perceived by the Swedish adoption world’s three stakeholders, the adoptive parents, the adoption organizations, and the adoptees, between 2021 and 2024 and in relation to transitional justice theories, with a focus on the issues of retributive and restorative justice. Full article

(This article belongs to the Special Issue Adoption Is Stranger than Fiction)

26 pages, 516 KiB

Open AccessArticle

Sustainability Struggle: Challenges and Issues in Managing Sustainability and Environmental Protection in Local Tourism Destinations Practices—An Overview

by Zorica Đurić, Drago Cvijanović, Vita Petek and Jasna Potočnik Topler

Sustainability 2025, 17(15), 7134; https://doi.org/10.3390/su17157134 - 6 Aug 2025

Abstract

This article aims to explore and analyze current issues and features of environmental protection in managing local tourism destinations based on the principles of sustainable development through the relevant literature and thus to provide an insight into major environmental measures and activities that should be implemented in practice, emphasizing the importance of environmental sustainability as a key factor in the development and success of local tourist destinations in today’s business environment. Qualitative methods were used, with the literature review based on content analysis by keywords. This particularly affects the business process efficiency and the participation of destination stakeholders and in many cases leads to a low level of environmentally sustainable destination practices. In addition to this theoretical approach, this study also has direct managerial implications for destination environmental business operations. An attractive and well-preserved environment is the primary factor of tourism and local tourism destination development and its success, as well as an integrated part of the tourism product. This study addresses a critical gap in the existing literature on environmental sustainability at local destinations, where prior work has often overlooked the integration of actionable, practice-oriented frameworks tailored for both researchers and practitioners. While theoretical insights into sustainable practices abound, there remains a scarcity of holistic analyses that bridge scholarly understanding with implementable strategies for on-the-ground application. To fill this void, our research provides a comprehensive overview and systematic analysis of current practices, with targeted emphasis on co-developing scalable frameworks for improving environmentally sustainable practices at local destinations. Full article

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34 pages, 3002 KiB

Open AccessArticle

A Refined Fuzzy MARCOS Approach with Quasi-D-Overlap Functions for Intuitive, Consistent, and Flexible Sensor Selection in IoT-Based Healthcare Systems

by Mahmut Baydaş, Safiye Turgay, Mert Kadem Ömeroğlu, Abdulkadir Aydin, Gıyasettin Baydaş, Željko Stević, Enes Emre Başar, Murat İnci and Mehmet Selçuk

Mathematics 2025, 13(15), 2530; https://doi.org/10.3390/math13152530 - 6 Aug 2025

Abstract

Sensor selection in IoT-based smart healthcare systems is a complex fuzzy decision-making problem due to the presence of numerous uncertain and interdependent evaluation criteria. Traditional fuzzy multi-criteria decision-making (MCDM) approaches often assume independence among criteria and rely on aggregation operators that impose sharp transitions between preference levels. These assumptions can lead to decision outcomes with insufficient differentiation, limited discriminatory capacity, and potential issues in consistency and sensitivity. To overcome these limitations, this study proposes a novel fuzzy decision-making framework by integrating Quasi-D-Overlap functions into the fuzzy MARCOS (Measurement of Alternatives and Ranking According to Compromise Solution) method. Quasi-D-Overlap functions represent a generalized extension of classical overlap operators, capable of capturing partial overlaps and interdependencies among criteria while preserving essential mathematical properties such as associativity and boundedness. This integration enables a more intuitive, flexible, and semantically rich modeling of real-world fuzzy decision problems. In the context of real-time health monitoring, a case study is conducted using a hybrid edge–cloud architecture, involving sensor tasks such as heartrate monitoring and glucose level estimation. The results demonstrate that the proposed method provides greater stability, enhanced discrimination, and improved responsiveness to weight variations compared to traditional fuzzy MCDM techniques. Furthermore, it effectively supports decision-makers in identifying optimal sensor alternatives by balancing critical factors such as accuracy, energy consumption, latency, and error tolerance. Overall, the study fills a significant methodological gap in fuzzy MCDM literature and introduces a robust fuzzy aggregation strategy that facilitates interpretable, consistent, and reliable decision making in dynamic and uncertain healthcare environments. Full article

(This article belongs to the Special Issue Advances in Multiple-Criteria Decision Making: New Trends and Applications)

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27 pages, 4506 KiB

Open AccessFeature PaperArticle

Interpretable Machine Learning Framework for Corporate Financialization Prediction: A SHAP-Based Analysis of High-Dimensional Data

by Yanhe Wang, Wei Wei, Zhuodong Liu, Jiahe Liu, Yinzhen Lv and Xiangyu Li

Mathematics 2025, 13(15), 2526; https://doi.org/10.3390/math13152526 - 6 Aug 2025

Abstract

High-dimensional prediction problems with complex non-linear feature interactions present significant algorithmic challenges in machine learning, particularly when dealing with imbalanced datasets and multicollinearity issues. This study proposes an innovative Shapley Additive Explanations (SHAP)-enhanced machine learning framework that integrates SHAP with advanced ensemble methods for interpretable financialization prediction. The methodology simultaneously addresses high-dimensional feature selection using 40 independent variables (19 CSR-related and 21 financialization-related), multicollinearity issues, and model interpretability requirements. Using a comprehensive dataset of 25,642 observations from 3776 Chinese A-share companies (2011–2022), we implement nine optimized machine learning algorithms with hyperparameter tuning via the Hippopotamus Optimization algorithm and five-fold cross-validation. XGBoost demonstrates superior performance with 99.34% explained variance, achieving an RMSE of 0.082 and R² of 0.299. SHAP analysis reveals non-linear U-shaped relationships between key predictors and financialization outcomes, with critical thresholds at approximately 10 for CSR_SocR, 1.5 for CSR_S, and 5 for CSR_CV. SOE status, EPU, ownership concentration, firm size, and housing prices emerge as the most influential predictors. Notable shifts in factor importance occur during the COVID-19 pandemic period (2020–2022). This work contributes a scalable, interpretable machine learning architecture for high-dimensional financial prediction problems, with applications in risk assessment, portfolio optimization, and regulatory monitoring systems. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence, Machine Learning and Optimization)

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23 pages, 3031 KiB

Open AccessArticle

Integrated Capuchin Search Algorithm-Optimized Multilayer Perceptron for Robust and Precise Prediction of Blast-Induced Airblast in a Blasting Mining Operation

by Kesalopa Gaopale, Takashi Sasaoka, Akihiro Hamanaka and Hideki Shimada

Geosciences 2025, 15(8), 306; https://doi.org/10.3390/geosciences15080306 - 6 Aug 2025

Abstract

Blast-induced airblast poses a significant environmental and operational issue for surface mining, affecting safety, regulatory adherence, and the well-being of surrounding communities. Despite advancements in machine learning methods for predicting airblast, present studies neglect essential geomechanical characteristics, specifically rock mass strength (RMS), which is vital for energy transmission and pressure-wave attenuation. This paper presents a capuchin search algorithm-optimized multilayer perceptron (CapSA-MLP) that incorporates RMS, hole depth (HD), maximum charge per delay (MCPD), monitoring distance (D), total explosive mass (TEM), and number of holes (NH). Blast datasets from a granite quarry were utilized to train and test the model in comparison to benchmark approaches, such as particle swarm optimized artificial neural network (PSO-ANN), multivariate regression analysis (MVRA), and the United States Bureau of Mines (USBM) equation. CapSA-MLP outperformed PSO-ANN (RMSE = 1.120, R² = 0.904 compared to RMSE = 1.284, R² = 0.846), whereas MVRA and USBM exhibited lower accuracy. Sensitivity analysis indicated RMS as the main input factor. This study is the first to use CapSA-MLP with RMS for airblast prediction. The findings illustrate the significance of metaheuristic optimization in developing adaptable, generalizable models for various rock types, thereby improving blast design and environmental management in mining activities. Full article

(This article belongs to the Section Geomechanics)

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14 pages, 719 KiB

Open AccessArticle

Recursive Interplay of Family and Biological Dynamics: Adults with Type 1 Diabetes Mellitus Under the Spotlight

by Helena Jorge, Bárbara Regadas Correia, Miguel Castelo-Branco and Ana Paula Relvas

Diabetology 2025, 6(8), 81; https://doi.org/10.3390/diabetology6080081 - 6 Aug 2025

Abstract

Objectives: Diabetes Mellitus involves demanding challenges that interfere with family functioning and routines. In turn, family and social context impacts individual glycemic control. This study aims to identify this recursive interplay, the mutual influences of family systems and diabetes management. Design: Data was collected through a cross-sectional design comparing patients, aged 22–55, with and without metabolic control. Methods: Participants filled out a set of self-report measures of sociodemographic, clinical and family systems assessment. Patients (91) were also invited to describe their perception about disease management interference regarding family functioning. We first examined the extent to which family variables grouped dataset to determine if there were similarities and dissimilarities that fit with our initial diabetic groups’ classification. Results: Cluster analysis results identify a two-cluster solution validating initial classification of two groups of patients: 49 with metabolic control (MC) and 42 without metabolic control (NoMC). Independent sample tests suggested statistically significant differences between groups in family subscales- family difficulties and family communication (p < 0.05). Binary logistic regression shed light on predictors of explained variance to no metabolic control, in four models: Sociodemographic, Clinical data, SCORE-15/Congruence Scale and Eating Behavior. Furthermore, groups differ on family support, level and sources of family conflict caused by diabetes management issues. Considering only patients who co-habit with a partner for more than one year (N = 44), NoMC patients score lower on marital functioning in all categories (p < 0.05). Discussion: Family-Chronic illness interaction plays a significant role in a patient’s adherence to treatment. This study highlights the Standards of Medical Care for Diabetes, considering caregivers and family members on diabetes care. Full article

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