Purpose: This study aimed to identify the incidence and related factors of delirium among cancer patients admitted to the general wards of a tertiary hospital. Methods: A retrospective analysis was performed on 9,749 adult cancer patients. Patients who screened positive for delirium were assessed using the Korean version of the Nursing Delirium Screening Scale (NuDESC). Data were analyzed using χ² tests, t-tests, and multiple logistic regression. Results: The incidence of delirium was 2.5% (n=243) based on screening results. Significant predictors included older age, longer hospital stays, emergency room admission, 90-day readmission, completion of life-sustaining treatment decisions, surgical department admission, altered consciousness (the strongest predictor), elevated white blood cell and blood urea nitrogen levels, and low albumin levels (p<.050).These 10 factors showed high discriminatory power for delirium (AUC=.91, 95% CI: 0.90~0.93). Conclusion The Nu-DESC should be actively utilized in clinical practice for early delirium detection. Nursing interventions must prioritize managing modifiable factors such as nutritional status, infection, and dehydration. Furthermore, case-sharing systems and regular education are essential to enhance nursing expertise and ensure patient safety.

This study aimed to develop a predictive model for the early identification of patients at risk of sepsis, using routinely available clinical information and laboratory test results collected during the initial phase of patient care. Methods: This retrospective analysis included electronic medical records of 22,400 adult patients who presented with suspected infection to a tertiary care university hospital in Korea between January 2013 and May 2024. Patients were classified according to Systemic Inflammatory Response Syndrome (score ≥ 2) or Quick Sequential Organ Failure Assessment (score ≥ 2), in combination with sepsis-related International Classification of Diseases, 10th revision codes. Four different machine learning models were trained and validated using five-fold cross-validation. In addition, Shapley additive explanations analysis was performed to interpret the contribution and clinical relevance of key predictive variables. Results: Among the evaluated models, CatBoost demonstrated the strongest predictive performance. Notably, platelet distribution width, alveolar–arterial oxygen difference, procalcitonin, and the arterial/alveolar oxygen ratio consistently emerged as major predictors. Importantly, several variables that did not reach statistical significance in univariate analysis nevertheless contributed substantially to overall model performance, highlighting the importance of complex, multidimensional interactions among clinical factors. Conclusion: These findings indicate that a model based on simple, routinely collected clinical data can achieve high predictive accuracy and strong generalizability. Such a tool may support early clinical decision-making by multidisciplinary teams, including nurses, across diverse real-world care settings. Further prospective studies are warranted to validate its clinical utility and to assess its potential effects on patient outcomes.

This study aimed to evaluate the effect of the TouchCare system, a digital health management system utilizing the internet of things (IoT), based on the usage levels of older adults. Methods: This is a secondary analysis of data from a quasi-experimental study examining the effects of an IoT-based digital healthcare system. Participants were equipped with the TouchCare application, a touch-tag, and context-aware artificial intelligence. Data on cognitive function, frailty, depressive symptoms, nutritional status, and fall efficacy were collected at baseline and after six months of using the system. The participants were divided into a high-engagement group (n = 22) and a low-engagement group (n = 24) based on how many days they used the application during the study. We used descriptive statistics, the paired t-test, the independent-samples t-test, and two-way mixed analysis of variance. Results: In total, 46 participants completed the evaluations (mean age, 76.6 years). Two-way mixed analysis of variance revealed no significant group-by-time interaction for cognitive function (p = .184), frailty (p = .338), depressive symptoms (p = .543), and nutritional status (p = .589). There was no significant difference in fall efficacy between the two groups (p = .091). The high-engagement group exhibited significant improvements in visuospatial and executive functions on the Montreal Cognitive Assessment (p = .029). Conclusion: The IoT-based mobile health management application demonstrated benefits in improving cognitive health among older adults. The findings suggest that active engagement with healthcare technology can positively affect health in this population, emphasizing the need for continuous support from nurses as health providers.

This study aimed to evaluate the effect of the TouchCare system, a digital health management system utilizing the internet of things (IoT), based on the usage levels of older adults. Methods: This is a secondary analysis of data from a quasi-experimental study examining the effects of an IoT-based digital healthcare system. Participants were equipped with the TouchCare application, a touch-tag, and context-aware artificial intelligence. Data on cognitive function, frailty, depressive symptoms, nutritional status, and fall efficacy were collected at baseline and after six months of using the system. The participants were divided into a high-engagement group (n = 22) and a low-engagement group (n = 24) based on how many days they used the application during the study. We used descriptive statistics, the paired t-test, the independent-samples t-test, and two-way mixed analysis of variance. Results: In total, 46 participants completed the evaluations (mean age, 76.6 years). Two-way mixed analysis of variance revealed no significant group-by-time interaction for cognitive function (p = .184), frailty (p = .338), depressive symptoms (p = .543), and nutritional status (p = .589). There was no significant difference in fall efficacy between the two groups (p = .091). The high-engagement group exhibited significant improvements in visuospatial and executive functions on the Montreal Cognitive Assessment (p = .029). Conclusion: The IoT-based mobile health management application demonstrated benefits in improving cognitive health among older adults. The findings suggest that active engagement with healthcare technology can positively affect health in this population, emphasizing the need for continuous support from nurses as health providers.

This study aimed to evaluate the effect of the TouchCare system, a digital health management system utilizing the internet of things (IoT), based on the usage levels of older adults. Methods: This is a secondary analysis of data from a quasi-experimental study examining the effects of an IoT-based digital healthcare system. Participants were equipped with the TouchCare application, a touch-tag, and context-aware artificial intelligence. Data on cognitive function, frailty, depressive symptoms, nutritional status, and fall efficacy were collected at baseline and after six months of using the system. The participants were divided into a high-engagement group (n = 22) and a low-engagement group (n = 24) based on how many days they used the application during the study. We used descriptive statistics, the paired t-test, the independent-samples t-test, and two-way mixed analysis of variance. Results: In total, 46 participants completed the evaluations (mean age, 76.6 years). Two-way mixed analysis of variance revealed no significant group-by-time interaction for cognitive function (p = .184), frailty (p = .338), depressive symptoms (p = .543), and nutritional status (p = .589). There was no significant difference in fall efficacy between the two groups (p = .091). The high-engagement group exhibited significant improvements in visuospatial and executive functions on the Montreal Cognitive Assessment (p = .029). Conclusion: The IoT-based mobile health management application demonstrated benefits in improving cognitive health among older adults. The findings suggest that active engagement with healthcare technology can positively affect health in this population, emphasizing the need for continuous support from nurses as health providers.

This study aimed to evaluate the effect of the TouchCare system, a digital health management system utilizing the internet of things (IoT), based on the usage levels of older adults. Methods: This is a secondary analysis of data from a quasi-experimental study examining the effects of an IoT-based digital healthcare system. Participants were equipped with the TouchCare application, a touch-tag, and context-aware artificial intelligence. Data on cognitive function, frailty, depressive symptoms, nutritional status, and fall efficacy were collected at baseline and after six months of using the system. The participants were divided into a high-engagement group (n = 22) and a low-engagement group (n = 24) based on how many days they used the application during the study. We used descriptive statistics, the paired t-test, the independent-samples t-test, and two-way mixed analysis of variance. Results: In total, 46 participants completed the evaluations (mean age, 76.6 years). Two-way mixed analysis of variance revealed no significant group-by-time interaction for cognitive function (p = .184), frailty (p = .338), depressive symptoms (p = .543), and nutritional status (p = .589). There was no significant difference in fall efficacy between the two groups (p = .091). The high-engagement group exhibited significant improvements in visuospatial and executive functions on the Montreal Cognitive Assessment (p = .029). Conclusion: The IoT-based mobile health management application demonstrated benefits in improving cognitive health among older adults. The findings suggest that active engagement with healthcare technology can positively affect health in this population, emphasizing the need for continuous support from nurses as health providers.

Background: Post-transplant diabetes mellitus (PTDM) is one of the most significant complications after transplantation. Patients with end-stage liver diseases requiring transplantation are prone to sarcopenia, but the association between sarcopenia and PTDM remains to be elucidated. We aimed to investigate the effect of postoperative muscle mass loss on PTDM development. Methods: A total of 500 patients who underwent liver transplantation at a tertiary care hospital between 2005 and 2020 were included. Skeletal muscle area at the level of the L3–L5 vertebrae was measured using computed tomography scans performed before and 1 year after the transplantation. The associations between the change in the muscle area after the transplantation and the incidence of PTDM was investigated using a Cox proportional hazard model. Results: During the follow-up period (median, 4.9 years), PTDM occurred in 165 patients (33%). The muscle mass loss was greater in patients who developed PTDM than in those without PTDM. Muscle depletion significantly increased risk of developing PTDM after adjustment for other confounding factors (hazard ratio, 1.50; 95% confidence interval, 1.23 to 1.84; P=0.001). Of the 357 subjects who had muscle mass loss, 124 (34.7%) developed PTDM, whereas of the 143 patients in the muscle mass maintenance group, 41 (28.7%) developed PTDM. The cumulative incidence of PTDM was significantly higher in patients with muscle loss than in patients without muscle loss (P=0.034). Conclusion Muscle depletion after liver transplantation is associated with increased risk of PTDM development.

Purpose: With an increase in the aging population, the number of patients with degenerative spinal diseases undergoing surgery has risen, as has the incidence of postoperative delirium. This study aimed to investigate the risk factors affecting postoperative delirium in older adults who had undergone spine surgery and to identify the associated biomarkers. Methods: This study is a prospective study. Data of 100 patients aged ≥ 70 years who underwent spinal surgery were analyzed. Demographic data, medical history, clinical characteristics, cognitive function, depression symptoms, functional status, frailty, and nutritional status were investigated to identify the risk factors for delirium. The Confusion Assessment Method, Delirium Rating Scale-R-98, and Nursing Delirium Scale were also used for diagnosing deliri-um. To discover the biomarkers, urine extracellular vesicles (EVs) were analyzed for tau, ubiquitin carboxy-terminal hydrolase L1 (UCH-L1),neurofilament light, and glial fibrillary acidic protein using digital immunoassay technology. Results: Nine patients were excluded, and data obtained from the remaining 91 were analyzed. Among them, 18 (19.8%) developed delirium. Differences were observed between partici-pants with and without delirium in the contexts of a history of mental disorder and use of benzodiazepines (p = .005 and p = .026, respectively). Tau and UCH-L1—concentrations of urine EVs—were comparatively higher in participants with severe delirium than that in partici-pants without delirium (p = .002 and p = .001, respectively). Conclusion These findings can assist clinicians in accurately identifying the risk factors before surgery, classifying high-risk patients, and predicting and detecting delirium in older patients. Moreover, urine EV analysis revealed that postoperative delirium following spinal surgery is most likely associated with brain damage.

Purpose: This study aimed to determine the effect of an exercise intervention on subjective cancer-related fatigue (CRF) and pro-inflammatory cytokine levels in breast cancer survivors (BCS). Methods: BCS with greater than moderate CRF (≥ 4) were recruited and randomly assigned to experimental or control groups. The experimental group participated in a 12-week exercise adherence program (Better Life after Cancer - Energy, Strength, and Support; BLESS). Interleukin (IL)-6 and tumor necrosis factor-α (TNF-α) levels were determined at 3 time points (M1: baseline, M2: post-intervention, and M4: 6 months after intervention). Subjective fatigue was measured using the Korean version of the revised Piper Fatigue Scale. Results: In this analysis of participants with physiological fatigue measures available (19 experimental, 21 control), there were no statistically significant differences in IL-6 (F = 1.157, p = 0.341), TNF-α levels (F = 0.878, p = 0.436), and level of fatigue (F = 2.067, p = 0.118) between the 2 groups at baseline. Fatigue in the experimental group showed statistically significant improvement compared to the control only at M2 (p = 0.022). There was no significant relationship between subjective and physiological fatigue at the 3 measurement points. Conclusion The BLESS intervention improved CRF in BCS immediately at post-intervention, and this study presents clinical feasibility for the management of CRF in BCS in the early survivorship phase who are already experiencing fatigue.