Everyone is aware that air and environmental pollutants are harmful to health. Among them, indoor air quality directly affects physical health, such as respiratory rather than outdoor air. However, studies that have examined the correlation between environmental and health information have been conducted with public data targeting large cohorts, and studies with real-time data analysis are insufficient. Therefore, this research explores the research with an indoor air quality monitoring (AQM) system based on developing environmental detection sensors and the internet of things to collect, monitor, and analyze environmental and health data from various data sources in real-time. It explores the usage of wearable devices for health monitoring systems. In addition, the availability of big data and artificial intelligence analysis and prediction has increased, investigating algorithmic studies for accurate prediction of hazardous environments and health impacts. Regarding health effects, techniques to prevent respiratory and related diseases were reviewed.

Background: Long commuting times have a negative impact on mental health. However, few studies have explored the relationship between commuting time and well-being based on urbanization by region. Our study examines this relationship as well as the effect of regional differences on Korean workers. Methods: We used data from the sixth Korean Working Conditions Survey. Commuting time and occupational factors were assessed using a questionnaire, and subjective well-being was assessed using the World Health Organization-5 Well-Being Index. Regions were divided into the cities and the provinces based on Korea’s administrative divisions. Logistic regression analysis was performed to investigate the association between commuting time and wellbeing. Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) for well-being were estimated, using participants commuting time of < 20 minutes as a reference group. Results: The total number of workers was 29,458 (13,855 men, 15,603 women). We found higher aORs for low well-being among workers with long commuting times (aOR, 1.23; 95% CI, 1.11–1.36 and aOR, 1.28; 95% CI, 1.16–1.42 for 60–79 and ≥ 80 minutes, respectively). When stratified by sex and region, higher aORs for low well-being were found only in the workers who lived in cities. Conclusion Long commuting time was negatively associated with well-being in Korean wage workers living in the cities. Policies for reducing commuting time should be discussed to address the mental health of workers, especially those living in metropolitan cities.

PURPOSE: Following determination of the maximum standardized uptake values (SUVmax) of the mediastinal lymph nodes (SUV-LN) and of the primary tumor (SUV-T) on ¹⁸F-FDG PET/CT in patients with non-small-cell lung cancer (NSCLC), the aim of the study was to determine the value of the SUV-LN/SUV-T ratio in lymph node staging in comparison with that of SUV-LN.METHODS: We retrospectively reviewed a total of 289 mediastinal lymph node stations from 98 patients with NSCLC who were examined preoperatively for staging and subsequently underwent pathologic studies of the mediastinal lymph nodes. We determined SUV-LN and SUV-R for each lymph node station on ¹⁸F-FDG PET/CT and then classified each station into one of three groups based on SUV-T (low, medium and high SUV-T groups). Diagnostic performance was assessed based on receiver operating characteristic (ROC) curve analysis, and the optimal cut-off values that would best discriminate metastatic from benign lymph nodes were determined for each method.RESULTS: The average of SUV-R of malignant lymph nodes was significantly higher than that of benign lymph nodes (0.79±0.45 vs. 0.36±0.23, P<0.0001). In the ROC curve analysis, the area under the curve (AUC) of SUV-R was significantly higher than that of SUV-LN in the low SUV-T group (0.885 vs. 0.810, P= 0.019). There were no significant differences between the AUCs of SUV-LN and of SUV-R in the medium and high SUV-T groups. The optimal cut-off value for SUV-R in the low SUV-T group was 0.71 (sensitivity 87.5 %, specificity 85.9 %).CONCLUSIONS: The SUV-R performed well in distinguishing between metastatic and benign lymph nodes. In particular, SUV-R was found to have a better diagnostic performance than SUV-LN in the low SUV-T group.
Area Under Curve ; Humans ; Lung Neoplasms ; Lung ; Lymph Nodes ; Methods ; Positron-Emission Tomography and Computed Tomography ; Retrospective Studies ; ROC Curve ; Sensitivity and Specificity