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In this article, a novel Lyapunov-based event-trigger mechanism is proposed to reduce the computation cost of model predictive control (MPC) algorithm for discrete-time nonlinear input-affine safety-critical systems. Unlike conventional approaches that require continuous error monitoring, the proposed mechanism leverages the predictive capability of MPC to determine triggering instants directly based on the evolution of the closed-loop Lyapunov function. Safety and stability are enforced by incorporating control barrier functions (CBFs) and control Lyapunov functions (CLFs) as constraints within the MPC optimization. Furthermore, the recursive feasibility of the proposed event-triggered MPC algorithm is rigorously analyzed, with special attention to the potential infeasibility caused by hard CBF constraints. Input-to-state practical stability (ISpS) of the resulting closed-loop system is also established. Simulation results demonstrate that the proposed event-triggered CBF-CLF-MPC algorithm effectively eliminates unnecessary controller updates, reducing computational consumption while maintaining tracking performance comparable to that of a conventional time-triggered MPC algorithm.

Recent evidence suggests that empiric prehospital administration of tenecteplase in out-of-hospital cardiac arrest is associated with decreased survival and lower rates of return of spontaneous circulation. While diagnostic uncertainty is a primary factor, operational hazards in austere environments must also be examined. This comment explores an underappreciated and potentially lethal operational hazard: the immediate chemical incompatibility between tenecteplase and dextrose-containing solutions. In chaotic pre-hospital settings, clinicians often rely on the cognitive heuristic of using dextrose lines to limit sodium intake, especially during cardiac emergencies. However, mixing tenecteplase with dextrose triggers instant crystallization and line occlusion. This effectively denies the patient the fibrinolytic agent while simultaneously compromising a critical intravenous access. To mitigate this risk, we propose three clinical safety barriers: physical segregation of dextrose from acute coronary syndrome kits, the mandatory implementation of a 20-mL normal saline flush before and after administration, and the use of point-of-care cognitive aids on drug packaging. Addressing the complex outcomes of prehospital thrombolysis requires mitigating simple, yet catastrophic, chemical errors. Environmental design and strict operational protocols are essential to ensure the safe delivery of tenecteplase in the field.

Complex I (NADH:quinone oxidoreductase, CI) is central to cellular aerobic energy metabolism. The L-shaped structure of CI is unique, where the hydrophilic arm is responsible for the electron transfer function and the membrane arm operates proton pumping. These two functional sites are spatially far apart yet functionally connected. This basic core subunit architecture is highly conserved from bacterial to mammalian CI. Here, to gain detailed mechanistic insight into the role of the membrane subunit ND2 in the coupling mechanism, we mutated several highly conserved residues in the middle of the membrane axis of NuoN, the E. coli CI homolog of ND2. To more precisely investigate the consequences of mutational effects on highly conserved residues, we purified each mutant CI and compared the mutational effects on electron transfer and proton pumping activity using our instant membrane reconstitution method with E. coli double knockout (DKO) membrane vesicles lacking both CI and alternative NADH dehydrogenase (NDH-2). Thre results were corroborated by conventional proteoliposome reconstitution experiments. We found that Lys247 and Lys395 are absolutely essential for both electron transfer and proton pumping activities, while about 50% reduction of NADH oxidase activity but no reduction in proton pumping activity was observed in Lys217, and no significant decrease was detected in Glu133. Furthermore, unexpectedly, we were able to purify an NuoN knockout (ΔNuoN) mutant, which contained stoichiometric peripheral subunits NuoB, NuoCD, NuoE, NuoF, NuoG, and NuoI; and a substoichiometric amount of NuoH and a reduced amount of quinone. However, surprisingly, this isolated ΔNuoN CI showed CI activities (~30% of the WT) after being reconstituted into DKO membranes but not into proteoliposomes. Later, we confirmed by blue native PAGE that the wild-type CI was partially formed from ΔNuoN CI by recruiting its missing membrane subunits that existed in DKO membranes. Our data strongly suggest that ND2/NuoN plays an essential role in the coupling mechanism in CI. CI is the entry respiratory chain enzyme and is central to cellular energy metabolism. Two highly conserved lysine residues in the center of the antiporter-like membrane subunit ND2 are essential for the coupling mechanism between electron transfer and proton translocation.

In this article, the zonotopic set-membership state estimation (ZSMSE) for cyber-physical systems (CPSs) is investigated in the presence of stealthy false data injection (FDI) sensor attacks. A set-membership state estimation approach is established in which the true state is enclosed by a parallelotope (a special class of zonotope) at each sampling instant. Due to the openness of the network transmission process, malicious attackers have the ability to modify sensor measurement information by injecting false data. Therefore, from the perspective of the defender, the condition for an attack to bypass the detector and destroy the ZSMSE is discussed. Specifically, the stealthiness definition for the FDI sensor attacks and the vulnerability definition for ZSMSE are given. Moreover, the necessary and sufficient condition for the ZSMSE to be vulnerable is derived. Subsequently, a watermarking-based protection strategy is proposed for vulnerable ZSMSE to ensure the validity of state estimation results under stealthy attacks. The watermarking matrix is designed to break the stealthiness of the attack while ensuring the convergence of the ZSMSE. Finally, a series of illustrative examples are provided to demonstrate the effectiveness of the proposed strategy.

Instant messaging applications are an integral part of everyday life, facilitating communication in various settings, including work and healthcare. This systematic review aims to analyze the healthcare contexts in which these applications are most commonly used (i.e., work organization, education, communication among healthcare personnel, communication with patients), with a particular focus on privacy issues raised by the handling of sensitive data on these platforms and how, or if, these issues are addressed. Following a systematic literature review process conducted according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, the articles were examined with the goal of extracting the aforementioned information. The results reveal that the majority of studies (82.6%) adopt commercial solutions to improve communication in medical field, with most of them using WhatsApp as the primary communication channel. Although the studies acknowledge the privacy risks introduced by these applications, the description of how such risks are mitigated or addressed is rarely reported in detail; what is consistently highlighted instead is the practical lack of clarity in existing regulations about the pratical applicability.

Generative AI is becoming integral to daily workflows, fostering a novel form of functional cognitive AI dependency distinct from pathological addiction. While emerging research acknowledges this phenomenon, the specific psychological mechanisms underpinning its development remain underexplored. Incorporating self-efficacy erosion into the reinforcement-based framework, this study investigates whether instant gratification and perceived AI efficacy as key drivers of AI dependency. We examine the model using Structural Equation Modeling (SEM) with cross-sectional data collected from 576 users who have engaged with AI. The results show that both instant gratification and efficient rewards are positively associated with individuals' AI dependency. Furthermore, users' self-efficacy erosion significantly mediates the positive relation, supporting the hypothesis that greater reliance on AI is related to lower self-belief and stronger AI dependency. Moderation analyses further indicate that task-domain self-efficacy and social norms strengthen these positive associations. These findings provide empirical support for a mechanism associated with functional AI dependency and offer insights for navigating human-AI interaction while promoting balanced AI adoption.

Postoperative peritoneal adhesion (PPA) is a pathological fibrous connection that forms between peritoneal surfaces after abdominal surgery, often resulting in infertility, intestinal obstruction, and increased difficulty during secondary operations. Despite numerous biomaterial-based strategies for adhesion prevention, their efficacy remains limited. To address this challenge, we synthesized N,N-dimethylaminopropylamine-conjugated hyaluronic acid (HA-DMAPA), reporting the first preparation of this conjugate. This polyampholyte rapidly forms a hydrogel under physiological conditions via reversible electrostatic intra- and intermolecular interactions between the tertiary amines of DMAPA and the unreacted carboxyl groups of HA, exhibiting pH-dependent gelation and degradation. The HA-DMAPA hydrogel demonstrated mechanical stability near its isoelectric point (pH 5-5.5) and accelerated degradation at physiological pH 7.4, indicating its potential as an inflammation-responsive polyampholyte. The material showed excellent injectability and instant self-healing due to strong shear-thinning properties, along with minimal cytotoxicity, reduced cell adhesion, enzymatic degradability, and remarkable hemocompatibility despite amine modification. In vivo, HA-DMAPA effectively reduced severe postoperative peritoneal adhesions in both rat cecum abrasion and partial hepatectomy models. Overall, this newly developed polyampholyte hydrogel derived from functionalized hyaluronan is simple, safe, and highly practical for surgical use, offering a promising strategy for preventing severe adhesions that remain challenging to manage in clinical practice.

As a National Centre for Pectus Surgery, our department receives referrals from across England. To strengthen perioperative support for patients undergoing the Nuss procedure in the RESTORE Trial (August 2024-May 2025), individual WhatsApp groups were introduced, reflecting growing evidence of the value of text-based communication in surgical care. Fifty-two groups were established, each averaging 6.7 members, including patients, relatives, secretarial, and research staff. These groups facilitated direct communication with the clinical team. All conversations were anonymised prior to analysis. Groups averaged an exchange of 67 messages: 20 from clinicians, 19 from patients, and 17 from family or friends. Patients initiated 28% of conversations and family members 20%, most often regarding pain management. Clinicians shared an average of 7.2 images per group, and patients shared 4, typically related to wounds or chest appearance. Mean clinician response time was 94 min, with daily message averages of 2 from clinicians, 2.5 from patients, and 1.6 from family. All patients reported satisfaction and would recommend this communication method. Instant messaging provided an effective, responsive, and low-burden means of enhancing perioperative care and reassurance for Nuss patients.

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Developmental Dyscalculia (DD) is a specific neurodevelopmental learning disability that significantly impairs an individual's ability to learn and process mathematical concepts. Given that preschool and primary school years represent critical windows for early intervention, this article aims to provide a comprehensive, state-of-the-art review of current assistive technology applications designed to support children with DD during this foundational educational period. The study systematically examines the landscape of current technological interventions and classifies them into four primary categories: (1) Mobile technology, (2) Digital-physical interaction technology, (3) Artificial intelligence (AI), and (4) Eye-tracking technology. The review evaluates the pedagogical mechanisms of these tools to understand how they address specific neurocognitive deficits. The analysis reveals that these technologies offer substantial core benefits for early intervention, specifically by enabling highly personalized learning pathways, enhancing student motivation, delivering instant feedback, and providing multisensory learning support. Conversely, the review identifies significant practical barriers to classroom implementation, including high deployment costs, the necessity for specialized teacher training, and the variable educational quality of existing applications. Assistive technologies demonstrate profound potential in building more inclusive, effective, and responsive learning environments for children with DD. Addressing the identified implementation challenges provides a clear roadmap for future interdisciplinary research, ensuring that these technological advances can be successfully and sustainably integrated into early childhood mathematics education.

Generative AI (genAI) is now embedded in higher education, yet its implications for undergraduate nursing students' decisions to attend voluntary on-campus learning alongside mandatory Clinical Learning Environment (CLE) activities remain underexplored, particularly in regulated clinical-hours contexts. To explore how undergraduate nursing students use genAI for coursework and assessment preparation and how this use influences perceived necessity to attend lectures, tutorials, and CLEs, with attention to variation across year levels. Exploratory-descriptive qualitative study conducted within a social-constructivist, interpretivist framework using applied thematic analysis. Eighteen students from a metropolitan Australian Bachelor of Nursing program. Semi-structured one-to-one interviews conducted on campus or via encrypted videoconference over one teaching period; audio-recorded and transcribed verbatim. Hybrid coding (sensitizing concepts and inductive expansion) with consensus meetings, reflexive memos, and member reflections. The study concluded based on analytical sufficiency rather than data saturation. Four themes captured a developmental journey. (1) Instant Expertise: genAI served as an on-demand clinical coach that condensed pre-CLE preparation and reduced anxiety, while risking attentional drift from tacit cues. (2) Strategic Presence: students triaged attendance-CLE as non-negotiable; lectures/tutorials as negotiable-with a developmental pattern (Year 1 attendance to decode "why," Year 2 efficiency-driven selectivity, Year 3 targeted presence or deliberate reinvestment). (3) Relational Recalibration: facilitated dialogue and peer debate in smaller forums fostered judgement and professional identity; large didactic lectures offered less added value, making AI feel more responsive. (4) From Reliance to Reflexivity: senior students verified genAI against local protocols and ethical frameworks, foregrounding person-centered care and policy currency. GenAI is transforming the emphasis from "seat time" to the "value of presence." Nursing programs should clearly define areas where in-person learning is essential, such as CLEs and facilitated case discussions, and incorporate genAI literacy and verification skills into their curricula. Future multi-site longitudinal research should investigate the outcomes of this innovative approach to presence.

The use of robotics and automation in self-driving laboratories (SDLs) can introduce additional safety complexities, beyond those already present in conventional research laboratories. Personal protective equipment (PPE) is an essential requirement for ensuring the safety and well-being of workers in all laboratories, self-driving or otherwise. Fires are another important risk factor in chemical laboratories. In SDLs, fires that occur close to mobile robots, which use flammable lithium batteries, could have increased severity. Here, we present Chemist Eye, a distributed safety monitoring system designed to enhance situational awareness in SDLs. The system integrates multiple stations equipped with RGB, depth, and infrared cameras, designed to monitor incidents in SDLs. Chemist Eye is also designed to spot workers who have suffered a potential accident or medical emergency, PPE compliance and fire hazards. To do this, Chemist Eye uses decision-making driven by a vision-language model (VLM). Chemist Eye is designed for seamless integration, enabling real-time communication with robots. Based on the VLM recommendations, the system attempts to drive mobile robots away from potential fire locations, exits, or individuals not wearing PPE, and issues audible warnings where necessary. It also integrates with third-party messaging platforms to provide instant notifications to lab personnel. We tested Chemist Eye with real-world data from an SDL equipped with three mobile robots and found that the spotting of possible safety hazards and decision-making performances reached 88% and 95%, respectively.

Digital tools are increasingly used to support recruitment and retention of participants in paediatric research, particularly since the COVID-19 pandemic. However, the extent of the evidence supporting this method in paediatric populations has yet to be evaluated. This scoping review aimed to review the literature on digital tools for recruitment and/or retention of participants in paediatric research, including emerging evidence following the pandemic. A scoping review was conducted following Joanna Briggs Institute methodology. We included peer-reviewed quantitative, qualitative, and mixed-method studies evaluating a digital tool for recruitment or retention in paediatric research in any patient population aged <13 years. Records were identified from systematic database searches with a librarian (EMBASE, MEDLINE, CINAHL), limited to English, from 2013 onwards (last search 03/07/2024), and manual searches. Records were screened and extracted independently in duplicate. The data were charted and narratively summarised. Sixty-one out of 4988 records were included. Most evaluations used an observational design; only 5 (8%) involved a randomised experiment. The host studies were mostly aiming to recruit children aged 5-12 years (n&#x2009;=&#x2009;42; 69%), with a predominantly health promotion (n&#x2009;=&#x2009;18; 30%), developmental (n&#x2009;=&#x2009;12; 20%), or oncology (n&#x2009;=&#x2009;9; 15%) focus. Most studies used multi-component digital interventions for recruitment (n&#x2009;=&#x2009;39/53; 74%) or retention (n&#x2009;=&#x2009;17/31; 55%). Social media (n&#x2009;=&#x2009;33/52; 62%) and websites (n&#x2009;=&#x2009;19/53; 36%) were most commonly used for recruitment, whereas text/instant messaging (n&#x2009;=&#x2009;17/31; 55%) and email (n&#x2009;=&#x2009;11/31; 36%) were the most common retention strategies. The estimates of recruitment and retention rates, and reach per digital tool varied widely between studies. Strategies in underserved populations reflected those used most commonly overall. Multi-component digital strategies were found to support a high rate of retention (84.1-90.7%) during pandemic restrictions. This scoping review highlights the broad array of digital tools that have been used to support recruitment and retention in studies of infants and children, including in subgroups of underserved populations and in response to the COVID-19 pandemic. Most evaluations were observational and examined&#xa0;multi-component digital interventions. The lack of studies with a robust analytical design in the literature signals a need for further high-quality, randomised, within-study evaluations following standardised reporting criteria. The protocol was registered on the Open Science Framework (OSF) at https://osf.io/ybfhr/. Registered on&#xa0;July 5 2024.

Goal: Electroencephalogram-based brain-computer interfaces (EEG BCIs) have broad applications in neurorehabilitation, clinical assessment, and assistive technologies. However, their practical deployment is severely limited by subject-specific calibration, which requires time-consuming data collection and model retraining for each user, significantly reducing usability. This reliance on calibration arises from the conventional "one-model-fits-all" strategy: "relying on a single general model to handle all data complexity like subject variability. When its limited generalization falls short, time must be spent on calibration to adapt the model." Methods: To address this limitation, we propose a trade-space-for-time strategy for calibration-free EEG decoding: "Instead of adapting one model to every user, we maintain a pool of compact models, including a general model and multiple biased models, where each biased model specializes in decoding a specific type of subject pattern. For a new input, the system automatically selects the most suitable model based on data characteristics, enabling instant adaptation without retraining." Compact deep learning models make this design feasible by allowing fast switching and low storage cost, which would be impractical with large-scale architectures. Results: Experiments on multiple public EEG datasets show that the proposed strategy achieves performance comparable to within-subject decoding: slightly higher in one dataset (0.7672 vs. 0.7601), nearly identical in another (0.7568 vs. 0.7572), and marginally lower in a third (0.8804 vs. 0.8888). Conclusions: These results demonstrate that our approach effectively eliminates calibration while preserving accuracy, providing a practical and scalable alternative for EEG BCIs. The framework also has potential applications in other neuroimaging modalities such as fMRI and fNIRS.

The development of noninvasive cancer diagnosis approaches may provide convenient, remote, painless diagnosis, instant healthcare, and postoperative follow-up. Label-free surface-enhanced Raman spectroscopy (SERS) is becoming a powerful approach for the detection of various potential biomarkers in cancer diagnosis; it avoids focusing on one or several specific targets, thus it may achieve the comprehensive and accurate diagnosis of cancer. In this work, a compact nano-superlattice is constructed as a label-free SERS substrate for the Raman analysis of urine samples from healthy people and lung cancer (LC) patients before and after surgery for the noninvasive diagnosis and postoperative monitoring of LC. Multiple chemometric methods, including principal component analysis (PCA), linear discriminant analysis (LDA), and orthogonal partial least-squares discriminant analysis (OPLS-DA) are applied for the classification of SERS spectra obtained from different samples. LDA and OPLS-DA outperform PCA to discriminate lung cancer preoperative patients from postoperative and healthy persons with higher efficiency. The high accuracy based on LDA and OPLS-DA is evaluated by calculating the area under the curve (AUC) of receiver operating characteristic (ROC) curves, demonstrating AUC values of 0.926 and 0.986 for LDA and OPLS-DA, respectively. In addition, the noninvasive SERS analysis of urine samples shows significant superiority for LC diagnosis over typical clinical biomarker tests such as carcinoembryonic antigen (CEA) testing in serum. These results demonstrate that label-free SERS associated with multivariate analysis is a promising tool for the noninvasive diagnosis and postoperative monitoring of LC patients.

Determining cause of death is fundamental for health services to guide the formulation of appropriate public policies to reduce and prevent mortality. In Brazil, some deaths still do not have their underlying cause defined, and in these cases, performing a complete diagnostic autopsy (CDA) is essential. However, there are challenges that hinder its performance, and in this scenario, minimally invasive tissue sampling (MITS) could be a promising alternative. Here, we explored the knowledge, acceptability, and attitudes towards CDA and MITS in Northeast Brazil. We conducted a cross-sectional study using structured questionnaires that were disseminated using snowball sampling through medical societies networks, social networks, instant messaging groups, and email. Chi-square tests or Fisher's exact tests were performed, as appropriate, to determine the difference between the five groups (i.e., pathologists, non-pathologist physicians, other health professionals, medical students, and the general population). 1,519 individuals participated, predominantly female (67.9%) with a median age of 41 (range 21 - 83) years. There was widespread recognition of the importance of CDA (79.6%) and a predisposition to authorize it among family members (67.0%). 1.5% reported knowing about MITS, and 83.4% believed that greater publicity for MITS would increase accessibility. Among physicians and pathologists (n&#x2009;=&#x2009;141), 52.2% agreed that trained professionals could perform the technique, and 79.3% agreed that MITS had a lower cost and required less hands-on time than CDA. Regarding implementation, blood (52.2%) and liver (26.1%) were identified as the easiest organs to sample, while brain (50.0%) and spleen (24.0%) were considered the most technically difficult. Although widely accepted among scholars in the field, MITS is little known outside of this environment. Investments in training, standardization of protocols for consent and conduct, and communication strategies that are sensitive to the sociocultural context are fundamental for its adoption as a complementary tool in determining the cause of death in countries like Brazil.

Background: Corticosteroid injections provide short-term relief for chronic subacromial bursitis but are associated with high recurrence rates. This study investigates the efficacy of a mobile health-supported home-based resistance exercise program compared with exercise education in patients with chronic recurrent subacromial bursitis after ultrasound-guided corticosteroid injections. Methods: Participants with chronic subacromial bursitis were assigned via computer-generated block randomization to either an intervention group receiving ultrasound-guided corticosteroid injections followed by a 12-week home-based exercise program (50 min strengthening and resistance/session, 5 days per week) supported via instant messaging applications, or a control group receiving the same injection followed by printed educational materials covering the same exercise protocol. Shoulder Pain and Disability Index (SPADI) scores, Visual Analog Scale (VAS) pain scores and active pain-free range of motion (ROM) were evaluated by a blinded assessor at weeks 4 and 12. Between-group comparisons were analyzed using two-way ANOVA after confirming normality and homoscedasticity. Results: Fifty-three patients (mean age: 55.6 &#xb1; 10.5 years; 47.2% female) were randomized to the intervention (n = 27) or control (n = 26) groups. Significant interaction effects were identified for SPADI (p = 0.040) and ROM (abduction: p = 0.036/ flexion: p = 0.032). Post hoc analysis revealed that the intervention group exhibited a significantly greater reduction in SPADI scores (p = 0.007, d = 0.72) and greater increase in abduction ROM (p = 0.004, d = 0.84) at 12 weeks; both gains surpassed the MCID. Conclusions: A mobile health-supported home-based resistance exercise program can significantly extend the benefits of corticosteroid injections in patients with chronic subacromial bursitis. Trial Registration: NCT06220643, registered 14 December 2023.

In pig-to-nonhuman primate islet transplantation, reliable, sensitive biomarkers are needed to detect graft damage at an early stage before irreversible islet loss occurs. In our study, we investigated donor-derived cell-free DNA (dd-cfDNA) as an early, noninvasive biomarker of graft injury by analyzing its correlation with porcine C-peptide levels, complement activation markers, and donor-specific antibodies (DSAs). Streptozotocin-induced diabetic cynomolgus monkeys received 50&#xa0;000-100&#xa0;000 IEQ/kg of intraportal islets from quadruple-knockout (QKO; GGTA1, CMAH, B4GALNT2, and A3GALT2) pigs. Cohort 1 received antithymocyte globulin (ATG), tacrolimus, mycophenolate mofetil (MMF), and anti-inflammatory agents (i.e., anakinra, adalimumab, and tocilizumab), whereas Cohort 2 received the same regimen plus rituximab and crovalimab. Graft function and immune responses were assessed by measuring porcine C-peptide levels, complement activation markers, histology, and dd-cfDNA kinetics. Cohort 1 showed transient porcine C-peptide secretion with marked dd-cfDNA elevation at 7 d postoperatively that coincided with complement activation (i.e., C5a and membrane attack complex (MAC)) and dense CD3+ T-cell and CD68+ macrophage infiltration, which resulted in early graft loss. Cohort 2 maintained stable C-peptide levels, lower dd-cfDNA levels, and reduced complement activation with improved graft preservation. Moreover, dd-cfDNA correlated negatively with C-peptide and positively with C5a but not with MAC. In both cohorts, DSA levels remained unchanged. Our study revealed that dd-cfDNA levels correlate with graft damage and C5a in QKO porcine islet xenografts, which corroborates dd-cfDNA utility as an early biomarker for predicting instant blood-mediated inflammatory reaction (IBMIR). These findings indicate that dd-cfDNA may be able to detect early islet xenograft damage.

Background: Nomophobia, the fear of being without a mobile phone, has become an increasing public health concern. While existing theories suggest that smartphones often serve as tools for emotional regulation, the situational mechanisms driving these compensatory behaviors remain under-explored. This study investigated how nomophobia levels interact with daily emotional fluctuations and busyness to influence smartphone-based coping patterns. Methods: We employed an intensive longitudinal approach combining objective smartphone tracking with a 4-week daily diary design. Thirty-seven participants were monitored, yielding 837 daily observations. Smartphone use was categorized into Instant Messaging (IM), Social Media Use (SMU), and Non-social Use (NSU). Multilevel linear regression analyzed the interaction effects on usage metrics. Results: Nomophobia significantly correlated with the duration and frequency of SMU, but not IM or NSU. A significant three-way interaction was observed: individuals with high levels of nomophobia exhibited a significantly increased frequency of overall usage, SMU and NSU when experiencing negative emotions during periods of low busyness. In contrast, low-nomophobia individuals maintained stable usage patterns regardless of situational stressors. Conclusions: By conceptualizing smartphone usage as a behavioral proxy for the coping process, this study provides preliminary evidence that nomophobia is associated with a situation-dependent coping pattern, primarily involving increased social media usage. These findings underscore the importance of integrating situational contexts and underlying coping processes to better understand and manage problematic smartphone use.