Work support tools built on generative artificial intelligence (AI), like Microsoft 365 Copilot and other AI assistants, are increasingly the object of experimentation in public sector. Yet, empirical research on workers' experiences with these tools remains limited. This article draws on 2.5 years of ethnographic fieldwork on AI innovation in two Finnish organizations and three cases of such tools to critically explore the positioning of users as ‘moral crumple zones’ who bear the burden of ensuring the effective and ethical use of these emerging tools. The findings capture five mechanisms that produce such zones in the use of generative AI tools for administrative and frontline work: (1) the assumption of human oversight and liability, (2) the immaturity of tested tools marketed as ‘sparring buddies’, (3) trial-and-error learning offloaded to users, (4) limited transparency of AI tools' development and (5) persuasive fallibility and the burden of vigilance placed on users. These challenges place users in roles that tend to shield the tools from broader scrutiny, meaning that users effectively absorb the burden of ethical and operational oversight and liability. This article contributes to understanding the often-thorny challenges around the uptake of generative AI tools in public sector and emphasizes the need for stronger accountability pathways—not only for users but also for other actors shaping these tools and their deployment. • AI-based tools are tested with promises to enhance work efficiency in public sector. • The proposed framework helps capture five mechanisms causing ‘moral crumple zones’. • AI-based tools' testers face epistemic, interpretative and operational opacity. • The findings reveal a misalignment between testers' control and accountability. • The study calls for real accountability pathways that go beyond individual users.

This investigation aims to estimate the diagnostic value of global longitudinal strain (GLS) and myocardial work (MW) indices for the identification of subclinical left ventricular (LV) systolic impairment (SLVSI) in individuals with simple obesity, obesity and prediabetes, and obesity and type 2 diabetes mellitus (T2DM). This investigation included 302 participants: 225 obese patients and 77 healthy individuals. LV GLS and MW-derived values, namely, the global constructive work (GCW), global work index (GWI), global wasted work (GWW), and global work efficiency (GWE), were analyzed. All individuals with obesity were classified into three groups: simple obesity, individuals with obesity and prediabetes, and individuals with obesity and T2DM. Comparisons of the LV GLS and MW were conducted among the four groups. The independent factors that affected the SLVSI in individuals with obesity were assessed using a logistic regression model. We conducted an ROC analysis to estimate the performance of MW in detecting SLVSI in individuals with obesity. Significant differences were detected in the GWI, GCW, LV GLS, GWW, and GWE among normal controls, individuals with simple obesity, individuals with obesity and prediabetes, and individuals with obesity and T2DM (P < 0.05). The combination of the GCW, GWW, GWI, and GWE resulted in the best AUC (0.896) for the determination of SLVSI in individuals with obesity. After adjustment for confounders, prediabetes or T2DM was independently associated with an absolute LV GLS < 20% in obese individuals. It is feasible to use LV GLS and MW-related parameters to determine SLVSI in individuals with obesity, individuals with obesity and prediabetes, and individuals with obesity and T2DM. Even in the preclinical stage prior to the onset of diabetes, prediabetes still has an incremental effect on SLVSI in obese patients.

What kinds of leader behaviors can simultaneously promote employee well-being and financial performance? Sustained organizational success requires advancing both outcomes, yet how leadership behaviors are associated with these outcomes remain unclear. Using four consecutive years of company-wide survey data from a Japanese homebuilder, we examined relationships between nine leader behaviors and branch-level employee well-being, as well as branch-level financial performance. After confirming the factor structure of the behavior items, we conducted hierarchical linear models and cross-lagged panel models. We found that three types of leader behaviors (employee development, communication, and sustainable work efficiency) were positively related to branch-level employee well-being, after controlling for branch demographics and year effects. In contrast, one behavior (workplace strategy, defined as clarifying the branch strategy and individual goals) was positively related to financial performance. In the longitudinal analyses, leader behaviors were not associated with subsequent employee well-being, whereas higher employee well-being was consistently associated with higher subsequent ratings of leader behaviors. We discussed priorities for allocating limited leadership resources to foster a happy workplace and contribute to financial outcomes, as well as the potential mechanisms underlying the observed associations between leader behaviors and both outcomes.

Excisia Photography, a professional photography service business in Kediri City, faced various operational obstacles hindering productivity, including slow file delivery to clients, difficulty in team coordination for file sharing, risk of data loss due to reliance on physical storage media, and inefficient manual archiving systems. This study aims to analyze the effect of Google Drive implementation as a cloud storage solution on the operational productivity of Excisia Photography, encompassing aspects of file distribution speed, team collaboration ease, data storage security, and work time efficiency. A descriptive quantitative approach was employed using total sampling of five core team members. Data were collected through a Likert-scale questionnaire (1–5) and semi-structured interviews. Results indicate that all productivity indicators achieved very good average scores, with the highest on the file delivery speed dimension (average 4.80), followed by file accessibility, data security, and work efficiency (each 4.60), and team collaboration (4.40). Interview data revealed a 35–40% reduction in project cycle completion time and nearly a twofold increase in production capacity following implementation. It is concluded that Google Drive has a significantly positive effect on the operational productivity of Excisia Photography and can serve as a strategic recommendation for similar photography businesses undergoing digital transformation.

BackgroundWorkforce needs are growing in older people care, exacerbated by the aging population. Technological solutions offer potential means to address this challenge. However, their effects on work and worktime management remain unclear, especially in the setting of assisted living services. Our aim was to assess the potential of two established technologies, automatic medicine dispensing and night‐time monitoring, in alleviating workforce needs in Finnish assisted living facilities.MethodsThe effects of the two technologies on workforce needs were studied using a convergent mixed‐methods design, incorporating quantitative time and motion analysis and qualitative analysis of two open‐ended survey questions. Linear regression analysis and data‐driven thematic analysis were employed. The quantitative and qualitative results were integrated and compared afterward.ResultsOur quantitative results indicated that medicine dispensing technology can streamline medicine management and administration, but effects on direct care time were not found. Use of night‐time monitoring technology was associated with more direct care during nights and reduced nightly staffing. The qualitative analysis highlighted medicine dispensers freeing up time for other work and night‐time monitoring reducing the need for nightly rounds. However, several technology‐related problems and a loss of medication competencies were perceived. Our results converged on several positive workforce effects, notably optimizing medication management and promoting individualized care during nights.ConclusionTechnology use could be promoted in assisted living services, when it demonstrably streamlines care work, increases work efficiency, allows for reduced staffing, or promotes individualized and patient‐centered care. National care policy can incentivize the use of similar technologies, for example, through staffing level legislation. However, care unit management should prepare for potential technology‐related errors and acute emergencies by upkeeping both technology and medication competencies of nurses and ensuring the presence of sufficient staff. Importantly, the potential efficiency gains must not come at the expense of quality or safety of care.

Impact of Nurse Workload on Work Efficiency, Patient Safety, and Job Satisfaction: A Cross-Sectional Study

Myocardial Work Indices in Olympic athletes: sex-specific reference values and the impact of sports disciplines.

BackgroundSecondary traumatic stress (STS) is a significant concern for firefighters due to their repeated exposure to traumatic events. In Saudi Arabia, cultural and organizational dynamics may amplify the risk of STS among firefighters' work performance. This heightened risk stems from culturally rooted stigma surrounding mental health, a lack of structured psychological support systems, and rigid hierarchical work environments that often discourage emotional expression or help-seeking behavior.ObjectiveThis study aims to investigate the prevalence of STS among Saudi firefighters, its impact on work efficiency and performance, and the moderating effects of personal, social, and organizational factors.MethodsA mixed-methods design was employed. The quantitative component included a cross-sectional survey of 304 male firefighters from different regions of Saudi Arabia, while the qualitative component involved semi-structured interviews with 22 participants Quantitative data were analyzed using descriptive statistics, Fisher's exact test, and stepwise regression, while qualitative data underwent thematic analysis guided by an inductive approach.ResultsThe results indicate a notable prevalence of psychological distress among Saudi firefighters. Significant associations were found between STS and several variables, including educational level, marital status, region of work, trauma exposure frequency, and the desire to transfer to another role. The study also highlighted the moderating influence of personal, social, and organizational factors on stress outcomes.ConclusionsThese findings underscore the pressing need for targeted mental health interventions and systemic organizational reforms to support firefighters' well-being and enhance occupational resilience.

The Charlson Comorbidity Index (CCI) is a validated mortality predictor in heart failure, but its utility in tracking subclinical left ventricular (LV) mechanics remains unclear. We evaluated the relationship between cumulative comorbidity burden, assessed by a modified CCI (mCCI), and LV mechanics using layer-specific speckle tracking echocardiography and non-invasive myocardial work (MW) analysis. Seventy-one stable outpatients with preserved LVEF (≥50%) and no overt heart failure or significant coronary artery disease were stratified by mCCI scores (adapted by excluding CAD and including hypertension): Group 1 (Low, 0-1), Group 2 (Intermediate, 2-3), Group 3 (High, ≥4). All underwent comprehensive echocardiography, layer-specific strain, and pressure-strain loop-derived MW analysis. While LVEF and epicardial GLS (Epi-GLS) were preserved across groups, Group 3 showed significantly impaired endocardial GLS (Endo-GLS) versus Group 1 (-19.7 ± 2.9%vs. -22.4 ± 2.0%, p = 0.003), with a stepwise decrease in the transmural strain gradient (Endo/Epi ratio; p < 0.001). Global Wasted Work (GWW) (p = 0.006) and Peak Systolic Dispersion (PSD) (p = 0.002) increased progressively, while Global Work Efficiency (GWE) was reduced in the high-risk group. On multivariate regression, mCCI independently predicted Endo-GLS (β = 0.236, p = 0.021) and was the sole independent predictor of GWE (β = -0.394, p = 0.002), irrespective of age and systolic blood pressure. During a median 6-month follow-up, Group 3 had a higher composite event rate than Groups 1 and 2 (44.4%vs. 7.4% and 11.1%; log-rank p = 0.001). An increased comorbidity burden, assessed by the mCCI, is independently associated with subclinical myocardial dysfunction-selective subendocardial impairment, increased wasted work, and mechanical inefficiency-suggesting the mCCI may flag early mechanical uncoupling in preserved LVEF. Although these abnormalities correlated with adverse outcomes, their modest discriminative value (AUC 0.60-0.62) underscores the hypothesis-generating nature of these findings, warranting validation in larger prospective cohorts.

This study investigated standard and advanced echocardiographic parameters in endurance athletes with different training profiles, and their association with exercise performance. Consecutive endurance athletes undergoing cardiological screening or orthopedic evaluation for knee injuries at Campus Bio-Medico University Hospital underwent advanced echocardiography and cardiopulmonary exercise testing. Athletes were categorized into three groups: (1) Long-Distance (marathon and ultramarathon runners, n = 30); (2) Mid-Distance (middle-distance runners, n = 27); and (3) Detrained (≥ 6 months training interruption, n = 31). Left ventricular ejection fraction did not differ among groups. The Long-Distance group had the highest stroke volume index, followed by the Mid-Distance and Detrained groups (p <0.001). Long-Distance athletes showed lower left ventricular global longitudinal strain (p = 0.003) and left atrial reservoir strain (p = 0.003) compared to the other groups, with no differences in right ventricular free wall strain. Myocardial work analysis showed higher work index and constructive work, and lower wasted work, leading to greater global work efficiency in the Long-Distance group (p <0.001). In multivariable linear regression analysis, stroke volume index (β = 1.02, p < 0.001) and global work efficiency (β = 1.00, p = 0.001) were independently associated with Peak VO2, whereas global longitudinal strain was not. Advanced echocardiography provides additional insights into the athlete's heart. Myocardial work indices reflect training-related cardiac adaptations, and left atrial reservoir strain is influenced by training status. These findings, together with the association of stroke volume index and global work efficiency with Peak VO2, support the integration of advanced echocardiographic parameters into athlete evaluation and monitoring.

This study aimed to evaluate an auto-verification system for serological markers of infectious diseases in a clinical laboratory, with the goal of ensuring reliable results while shortening the turnaround time for reports, reducing the need for human resources, and improving work efficiency. Based on the guidelines and clinical requirements, personalized auto-verification rules were established. Then, the serological marker results of 144,927 samples (including hepatitis B, hepatitis C, Treponema pallidum, and human immunodeficiency virus) were used for verification, and the performance of the auto-verification system was evaluated. The auto-verification system enabled our laboratory to shorten the turnaround time by 21.13% (from 369.54 minutes to 291.47 minutes) while increasing the sample volume. The pass rate, sensitivity, specificity, false positive rate, and false negative rate of the auto-verification system were 54.81%, 100%, 54.99%, 45.01% and 0%, respectively. Further analysis of the reasons for the failure of auto-verification revealed that a portion of the samples were blocked due to technical issues with the laboratory information system. Further improvements are needed in the future. Establishing an appropriate auto-verification system based on the actual situation of the laboratory is an important means to ensure medical safety, while improving the efficiency of the clinical laboratory and reducing labor costs.

The development of the Electronic-Based Government System (SPBE) requires the integration of effective, safe, and efficient digital services for the State Civil Apparatus (ASN). One of the problems faced by local governments is the fragmentation of the application system that requires users to perform repeated authentication, thereby reducing work efficiency and increasing information security risks. This study aims to analyze the implementation of the Single Sign-On (SSO) System in the integration of ASN digital services at the Sidoarjo Regency Communication and Information Office. The research method used is a descriptive qualitative approach with data collection techniques through observation, interviews, and documentation studies during internship activities. The analysis of policy implementation was carried out using the Edward III model which includes aspects of communication, resources, disposition, and bureaucratic structure. The results of the study show that the implementation of SSO is able to increase the efficiency of digital service access, simplify the authentication process, and support the integration of ASN's internal applications. However, the implementation of SSO still faces obstacles in the form of limited digital literacy for some users, data insynchronization between applications, and the need to strengthen information security governance. Therefore, optimizing SSO implementation requires increasing human resource capacity, integrating more integrated databases, and socializing and strengthening internal regulations on an ongoing basis

Tree barriers are the key factors of current transmission line failures, early discovery of tree barriers hidden dangers, the implementation of forest clearing tasks on transmission lines is the current power inspectors need to pay attention to the key issues. The article is based on the inclined photogrammetry technology to obtain the three-dimensional data of transmission lines, and construct a real-time database to realize the standardized management of three-dimensional data. Then the Mean Shift algorithm is used to preprocess the remote sensing data, and the forest diameter measurement system and transmission line forest clearing program are designed. QDN Power Supply Bureau was selected as a research sample to verify the effectiveness of the application of the above methods. The study showed that the RMSE of the breast diameter monitoring results ranged from 4.30% to 5.05%, and the reduction of forced outage rate of transmission lines of 110kV and above in the power grid could be up to 72.28%, and the overall work efficiency was improved by about 5.14 times. Therefore, actively realizing the optimization of transmission line forest clearing tasks can ensure the stable operation of transmission lines and provide basic support for ensuring the power supply of the grid.

With the speedy development of information technology, smart agriculture has become the key to the transformation and upgrading of modern agriculture. To improve the precision and practicality of data analysis, a data analysis model for grain harvesters based on a combination of K-means and Apriori is designed. This model collects grain harvester data in real-time, uses the K-means for preliminary clustering, and integrates Apriori algorithms to dynamically adjust clustering centers to improve clustering accuracy. At the same time, the model introduces ResNet to extract image features of grain harvesters, thereby enhancing the comprehensiveness of data analysis. Comparative experiments show that the algorithm has a high adjusted Rand index of 0.92, an F-value of 0.89, a convergence time of only 12.4 seconds, and a clustering accuracy of 95% for agricultural machinery databases. The analysis of actual operational data of grain harvesters shows that their faults are concentrated in the transmission device, accounting for up to 45%. When operating under high load, the work efficiency drops sharply, and when the rated power exceeds 30%, the work efficiency is only 72%. When working in a wet and muddy environment, the failure rate reaches 42.8%. From the above results, the data analysis model for grain harvesters based on the combination of K-means and Apriori algorithm proposed in the study can perform cluster analysis on the data of grain harvesters, laying a solid foundation for the sustainable development of smart agriculture.

Business Intelligence platforms have become indispensable in modern organizational workflows, yet most productiongrade tools demand persistent internet access, vendor-managed servers, and considerable technical proficiency. These constraints effectively exclude a wide range of users, from small-business analysts to data teams operating in air-gapped environments. VisoryBI was built specifically to remove those barriers. It is an offline-capable, browser-native dashboard builder implemented as a Progressive Web Application that performs all computation, storage, and rendering on the client device without any backend infrastructure; after the initial load, the application operates without any network connection. The platform ships with more than seventeen chart types, sixteen domain-specific dashboard templates equipped with intelligent field-mapping, an integrated statistical engine performing Z-Score and Interquartile Range anomaly detection alongside linearregression forecasting, and a client-side Natural Language Processing interface that lets users query their datasets in plain English. Persistence is handled through a two-tier storage model pairing Zustand-managed localStorage for lightweight state with IndexedDB for large datasets. Export pipelines support PDF, PNG, PowerPoint, and CSV output. A three-tier role-based access control system governs who may create, modify, or view content. Independent end-to-end testing across all modules recorded an overall working efficiency of approximately ninety-seven percent.

Social media platforms like Facebook have become key spaces for dynamic, multimodal communication, where users combine text, images, emojis, and other resources to construct meaning. In Filipino digital discourse, these practices reflect rich linguistic creativity and relational work, shaping how identity, social interaction, and meaning are negotiated online. This study examines the multimodal communicative practices of Filipino Facebook users, focusing on how linguistic and non-linguistic resources interact to construct meaning in computer-mediated discourse. Drawing on a corpus of 315 anonymized expressions from publicly accessible Facebook posts, the study employs a qualitative discourse-analytic approach grounded in multimodal discourse analysis and relational work theory. Methodological rigor is enhanced through brief interpretive interviews for communicative validation and expert linguistic review by two Filipino language scholars. The analysis identifies key linguistic features, including word-formation processes (blending, clipping, affixation, reduplication), sound-based expressions (interjections, letter elongation, onomatopoeia), non-standard orthography, and creative colloquial forms such as reverse spelling (sakalam from malakas). These linguistic resources co-occur with non-linguistic elements such as emojis, emoticons, GIFs, stickers, and images. Together, these multimodal resources perform expressive, phatic, humorous, and affiliative functions that support emotional clarity, relational work, and interactional efficiency in text-based digital environments. The findings demonstrate how Filipino users mobilize culturally specific multimodal repertoires, exemplified by emoji-anchored code-mixing (e.g., Favorite ko yan) and affectively charged respellings, to manage affect and social alignment in everyday digital interaction. By focusing on Filipino Facebook discourse, this study contributes to computer-mediated communication research by highlighting the culturally situated nature of multimodal meaning-making and challenging Anglocentric assumptions in digital discourse studies. The findings also offer implications for digital literacy education in multilingual Southeast Asian contexts.

BACKGROUND Laparoscopic gastrointestinal surgery instruments are complex, and traditional management models rely on nurses’ personal experiences, which can easily lead to intraoperative interruptions. Standardized instrument settings aim to improve team collaboration, efficiency, and surgical safety through a unified process. AIM To explore the application of an operating room nurse-led standardized laparoscopic instrument setup model to optimize laparoscopic gastrointestinal surgery. METHODS A total of 120 patients undergoing laparoscopic gastrointestinal surgery between June 2022 and June 2025 were divided into observation (n = 60) and control (n = 60) groups using the random number table method. The control group adopted the traditional instrument management model. The observation group implemented a standardized instrument setup (SIS) scheme formulated under the leadership of operating room nurses. Surgical efficiency indicators, clinical outcome indicators, postoperative complication rates, team subjective evaluations, and cognitive load levels were compared between groups. RESULTS Compared with the control group, the observation group showed shorter median operating-room occupancy time (185.6 ± 28.4 minutes vs 205.3 ± 32.1 minutes, P &lt; 0.001), surgical preparation time (18.5 ± 3.2 minutes vs 25.8 ± 4.7 minutes, P &lt; 0.001), and core surgical time (152.7 ± 25.6 minutes vs 168.9 ± 30.3 minutes, P = 0.001). Unplanned interruptions decreased (4.6 ± 1.5 events per case to 1.2 ± 0.8 events per case, P &lt; 0.001), including instrument-related interruptions (2.8 ± 1.1 to 0.5 ± 0.7, P &lt; 0.001) and communication-related interruptions (1.1 ± 0.8 to 0.3 ± 0.5, P &lt; 0.001). Effective instrument handover rate increased (95.2% to 99.1%, P &lt; 0.001), and mean handover time decreased (2.8 ± 0.6 seconds to 1.5 ± 0.3 seconds, P &lt; 0.001). Post-operative overall complication rate reduced (25.0% to 10.0%, χ ² = 4.675, P = 0.031). CONCLUSION Nurse-led SIS can significantly operative efficiency, reduce invalid operations and unplanned interruptions, and improve the overall collaborative tacit understanding and work efficiency, supporting further clinical promotion and application.

The article explores the social and educational context of the rapid development of artificial intelligence (AI) and examines its growing impact on contemporary society and modern education systems. The study outlines the evolution of AI from a theoretical concept to a widely applied technological solution that supports automation, data analysis, and decision-making processes across multiple sectors. Particular attention is given to the role of AI in transforming communication practices, especially through the use of conversational agents such as chatbots, which simulate human interaction and enhance user experience in digital environments. The paper analyzes the conceptual foundations of artificial intelligence, including its interdisciplinary nature, drawing from computer science, cognitive science, psychology, and philosophy. It also presents key areas of AI application, such as machine learning, natural language processing, computer vision, and expert systems, emphasizing their relevance in both educational and professional contexts. The author highlights the increasing integration of AI tools into educational processes, where they contribute to personalized learning, accelerated knowledge acquisition, and improved access to information resources. A significant part of the study is devoted to the practical use of AI systems, particularly ChatGPT and similar platforms, in supporting research activities. These tools facilitate literature searches, data processing, and problem-solving, thereby enhancing the efficiency of academic work. At the same time, the article addresses the limitations of AI-generated content, including the risk of superficial accuracy and the necessity of critical evaluation by users. The research also considers broader social implications of AI implementation, including its influence on the labor market, where automation may lead to the transformation or replacement of certain professions. Concerns related to ethical, psychological, and sociological aspects are discussed, highlighting the need for responsible and balanced integration of AI technologies. In conclusion, the article emphasizes that while artificial intelligence offers significant opportunities for innovation and development, it also requires a thoughtful and critical approach to its use in education and society. The formation of digital and analytical competencies becomes essential for effective interaction with AI-driven systems, ensuring that technological progress contributes to sustainable and human-centered development.

To evaluate the independent and combined value of global longitudinal strain (GLS) and global work efficiency (GWE) for predicting quantitative flow ratio (QFR) in patients with ischemic heart disease (IHD). In a prospective study of 222 patients with stable IHD, all participants underwent coronary angiography with QFR analysis and comprehensive 2D speckle-tracking echocardiography. LASSO regression for variable selection, multivariable logistic regression for association analysis, restricted cubic splines for non-linearity assessment, and SHAP analysis for model interpretation were employed. Patients with normal QFR (≥ 0.80) showed significantly better GLS (-17.84 ± 2.34% vs -14.47 ± 2.65%, P < 0.01) and GWE (94.00[91.00-95.00]% vs 91.00[88.00-93.00]%, P < 0.01) compared to those with abnormal QFR. LASSO regression identified both GLS (OR = 2.45, 95%CI 1.82-3.30) and GWE (OR = 1.31, 95%CI 1.15-1.50) as remaining independently associated with normal QFR after full adjustment. Both relationships demonstrated significant non-linearity (P < 0.05). GLS alone showed excellent diagnostic accuracy (AUC = 0.862), while adding GWE provided only marginal improvement (AUC = 0.872, P = 0.15). SHAP analysis confirmed GLS as the most important predictor, with good model generalizability (AUC = 0.838) upon validation. GLS is a powerful independent predictor of coronary hemodynamics assessed by QFR in IHD patients. Although GWE shows an independent association, its incremental value beyond GLS is limited, establishing GLS as the primary echocardiographic parameter for non-invasive stratification of functionally significant coronary artery disease.

Migraine is a major source of disability in Korea, yet many patients remain underdiagnosed or undertreated. Traditional epidemiological studies are limited by recall bias and incomplete patient-reported outcomes. App-based headache diaries offer real-time, repeated measures of symptoms and treatment, enabling large-scale assessment to better understand migraine burden and treatment response. This study aimed to evaluate the large-scale implementation of a nationally distributed smartphone "Headache Diary" mobile application and describe migraine burden, acute treatment response, and functional impairment in real-world practice in Korea. We conducted a retrospective, non-interventional analysis using self-reported data from the nationwide "Headache Diary" mobile application, developed by the Korean Headache Society. We included users who submitted at least one headache report between December 2020 and January 2025. Migraine/probable migraine were identified using an algorithm based on medication use and ICHD-3 criteria, while the remaining participants were classified into a non-migraine group (including tension-type headache or unclassifiable headache disorders). Acute-treatment failure was defined as pain relief reported after 2 hours or no effect. We also analyzed clinical characteristics, acute treatment patterns, and functional impairment associated with headache attacks. Of 23,713 users, 17,913 (75.5%) had migraine or probable migraine. Migraine patients experienced more severe headache intensity (55.5% vs. 13.7%), higher rates of premonitory symptoms (67.7% vs. 29.5%), more frequent identification of triggers (89.9% vs. 61.2%), and greater use of acute medications (73.1% vs. 23.8%) compared to non-migraine users. Among 211,302 headache attacks analyzed, 155,894 attacks were migraine, and 96,742 attacks were available for acute treatment effect assessment. Of the 86,301 triptan-treated attacks, 40.3% resulted in acute treatment failure. Adolescents (10-19 years) showed the highest failure rate (62.1%). Notably, attacks with premonitory symptoms (odds ratio [OR] 0.91, 95% CI 0.89-0.94), aura (OR 0.79, 95% CI 0.75-0.83), or potential triggers (OR 0.93, 95% CI 0.91-0.96) had lower odds of treatment failure. Functional impairment was reported in 36.6% of attacks, most as decreased work or study efficiency (21.0%) and reduced household activity efficiency (17.2%). Nationwide, app-based data revealed that migraine patients in Korea frequently experience severe attacks, premonitory symptoms, and triggers, with substantial rates of acute treatment failure and functional impairment. These findings highlight the need for earlier recognition and timely intervention and suggest that real-time digital platforms can provide valuable insights for patient-centered migraine management. Not applicable.