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: Weight gain after kidney transplantation is a critical factor that can lead to poor outcomes with cardiovascular complications. Many studies have been conducted to identify predictive markers of future weight changes at the time of transplant. Recently, circulating exosomes and its contents including miRNAs and proteins have attracted attention as potential biomarkers. In this pilot study, we investigated exosomal proteins and weight change after kidney transplant. Methods: Recipients (n=10) were classified into two groups; weight gainers (n=5, 9.7±4.4kg) and weight losers (n=5, -6.4±1.8kg) based on their weight changes at 12-months posttransplant. Based on the exosomal protein profiles obtained by the LC-MS/MS, differentially expressed proteins were identified between the groups. Results: Concentration and the mean size of exosomes significantly increased at 12-months compared to the baseline (p=.009) in the total group. Eleven exosomal proteins were found at the baseline as differentially expressed between the two groups. In the weight gain group, complement proteins including HV169, C3, C4B, and C4A, were significantly upregulated. Conclusion Our pilot study suggests that exosomal complementary proteins are associated with weight gain after kidney transplantation. Further studies are needed to clarify the role of these exosomal proteins in the underlying mechanisms of weight changes in kidney transplant recipients.

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.

PURPOSE: The awareness of hereditary breast and ovarian cancer (HBOC) and BRCA testing is increasing in Korea. Compared to the sizable research on HBOC knowledge among breast cancer women, studies in the ovarian cancer population are limited. This paper aimed to investigate the level of knowledge of hereditary ovarian cancer and anxiety in women diagnosed with serous ovarian cancer in Korea and determine differences in the knowledge and anxiety according to whether genetic testing was undertaken and whether BRCA1 or BRCA2 mutations were present.METHODS: Using a descriptive research design, a cross-sectional survey was conducted on 100 women diagnosed with serous ovarian cancer at N hospital in Gyeonggi-do, Korea, from July to November 2018. The collected data were analyzed by descriptive statistics, independent t-tests, one-way analysis of variance, and Pearson's correlation coefficient using the SPSS 21.0 program.RESULTS: The hereditary ovarian cancer-related knowledge score was mid-level (mean score 8.90±3.29 out of a total of 17), as was the state anxiety level was mid-level (mean score 47.96±3.26 out of possible score range of 20–80). Genetic knowledge of hereditary ovarian cancer was associated with age, education, occupation, genetic counseling, and BRCA mutations. There were no statistically significant factors related to anxiety and there were no statistically significant correlations between knowledge level and anxiety.CONCLUSION: More comprehensive education on gene-related cancer is needed for ovarian cancer patients, especially for items with low knowledge scores. A genetic counseling protocol should be developed to allow more patients to alleviate their anxiety through genetic counseling.
Anxiety ; Breast ; Breast Neoplasms ; Cross-Sectional Studies ; Education ; Female ; Genetic Counseling ; Genetic Testing ; Gyeonggi-do ; Hereditary Breast and Ovarian Cancer Syndrome ; Humans ; Korea ; Occupations ; Ovarian Neoplasms ; Research Design