• Safety and Health at Work
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• v.10 no.1
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• pp.95-102
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• 2019

Background: In a cluster-randomized trial, the Safety and Health Involvement For Truck drivers intervention produced statistically significant and medically meaningful weight loss at 6 months (-3.31 kg between-group difference). The current manuscript evaluates the relative impact of intervention components on study outcomes among participants in the intervention condition who reported for a post-intervention health assessment (n = 134) to encourage the adoption of effective tactics and inform future replications, tailoring, and enhancements. Methods: The Safety and Health Involvement For Truck drivers intervention was implemented in a Web-based computer and smartphone-accessible format and included a group weight loss competition and body weight and behavioral self-monitoring with feedback, computer-based training, and motivational interviewing. Indices were calculated to reflect engagement patterns for these components, and generalized linear models quantified predictive relationships between participation in intervention components and outcomes. Results: Participants who completed the full program-defined dose of the intervention had significantly greater weight loss than those who did not. Behavioral self-monitoring, computer-based training, and health coaching were significant predictors of dietary changes, whereas behavioral and body weight self-monitoring was the only significant predictor of changes in physical activity. Behavioral and body weight self-monitoring was the strongest predictor of weight loss. Conclusion: Web-based self-monitoring of body weight and health behaviors was a particularly impactful tactic in our mobile health intervention. Findings advance the science of behavior change in mobile health intervention delivery and inform the development of health programs for dispersed populations.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.32 no.3
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• pp.118-125
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• 2021

Objectives: Childhood adversity is a risk factor for anxiety symptoms, but it affects anxiety symptoms in attention-deficit/hyperactivity disorder (ADHD). The current study aimed to examine the association between childhood adversity and anxiety symptoms in participants with and without ADHD. Methods: Data were obtained from a school-based epidemiological study of 1017 randomly selected children and adolescents. The ADHD and non-ADHD groups were divided using the Diagnostic Interview Schedule for Children Predictive Scale (DPS). The DPS was also used to assess comorbidities such as anxiety and mood disorders. The childhood adversities were assessed using the Early Trauma Inventory Self Report-Short Form, and the anxiety symptoms were assessed using the Screen for Child Anxiety Related Disorders. Linear and logistic regression models were used to investigate the association between childhood adversity and anxiety in the ADHD and non-ADHD groups with adjustments for age and sex. Results: This study found that the ADHD group did not show any significant association between anxiety symptoms and childhood adversities, whereas the non-ADHD group always showed a significant association. In a subgroup analysis of the non-ADHD group, the normal group without any psychiatric disorders assessed with DPS demonstrated a statistically significant association between childhood adversities and anxiety symptoms. These results were consistent with the association between childhood adversities and anxiety disorders assessed using DPS, as shown by logistic regression. Conclusion: The association between anxiety symptoms and childhood adversities statistically disappears in ADHD; ADHD may mask or block the association. Further longitudinal research is necessary to investigate this relationship.

• Clinical and Experimental Reproductive Medicine
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• v.48 no.2
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• pp.163-173
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• 2021

Objective: This study aimed to characterize a validated model for predicting oocyte retrieval in controlled ovarian stimulation (COS) and to construct model-based nomograms for assistance in clinical decision-making regarding the gonadotropin protocol and dose. Methods: This observational, retrospective, cohort study included 636 women with primary unexplained infertility and a normal menstrual cycle who were attempting assisted reproductive therapy for the first time. The enrolled women were split into an index group (n=497) for model building and a validation group (n=139). The primary outcome was absolute oocyte count. The dose-response relationship was tested using modified Poisson, negative binomial, hybrid Poisson-E_max, and linear models. The validation group was similarly analyzed, and its results were compared to that of the index group. Results: The Poisson model with the log-link function demonstrated superior predictive performance and precision (Akaike information criterion, 2,704; λ=8.27; relative standard error (λ)=2.02%). The covariate analysis included women's age (p<0.001), antral follicle count (p<0.001), basal follicle-stimulating hormone level (p<0.001), gonadotropin dose (p=0.042), and protocol type (p=0.002 and p<0.001 for short and antagonist protocols, respectively). The estimates from 500 bootstrap samples were close to those of the original model. The validation group showed model assessment metrics comparable to the index model. Based on the fitted model, a static nomogram was built to improve visualization. In addition, a dynamic electronic tool was created for convenience of use. Conclusion: Based on our validated model, nomograms were constructed to help clinicians individualize the stimulation protocol and gonadotropin doses in COS cycles.

• Journal of Chest Surgery
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• v.55 no.3
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• pp.214-224
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• 2022

Background: Studies of the prognostic role of circulating tumor cells (CTCs) in early-stage non-small cell lung cancer (NSCLC) are still limited. This study investigated the prognostic power of CTCs from the pulmonary vein (PV), peripheral blood (PB), and bone marrow (BM) for postoperative recurrence in patients who underwent curative resection for NSCLC. Methods: Forty patients who underwent curative resection for NSCLC were enrolled. Before resection, 10-mL samples were obtained of PB from the radial artery, blood from the PV of the lobe containing the tumor, and BM aspirates from the rib. A microfabricated filter was used for CTC enrichment, and immunofluorescence staining was used to identify CTCs. Results: The pathologic stage was stage I in 8 patients (20%), II in 15 (38%), III in 14 (35%), and IV in 3 (8%). The median number of PB-, PV-, and BM-CTCs was 4, 4, and 5, respectively. A time-dependent receiver operating characteristic curve analysis showed that PB-CTCs had excellent predictive value for recurrence-free survival (RFS), with the highest area under the curve at each time point (first, second, and third quartiles of RFS). In a multivariate Cox proportional hazard regression model, PB-CTCs were an independent risk factor for recurrence (hazard ratio, 10.580; 95% confidence interval, 1.637-68.388; p<0.013). Conclusion: The presence of ≥4 PB-CTCs was an independent poor prognostic factor for RFS, and PV-CTCs and PB-CTCs had a positive linear correlation in patients with recurrence.

• Journal of muscle and joint health
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• v.29 no.3
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• pp.205-217
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• 2022

Purpose: This study aimed to investigate the factors affecting the subjective health status (SHS) of low-income older adults living alone. Methods: This is a cross-sectional correlational study using secondary data analysis. Sociodemographic and health-related characteristics were included in this analysis. The health-related characteristics were categorized into three domains: physical, characterized by the number of chronic diseases and fall-related factors, timed up and go, and grip strength; psychological, in terms of depression and loneliness; and social, in terms of social support. Data were analyzed using descriptive analysis, t-test, ANOVA, Pearson's correlation coefficient, and multiple linear regression analysis. Results: The mean SHS score was 2.46 out of five. Several factors influenced the SHS of low-income older adults living alone, including sex, age, level of education, monthly income, and the three domains. Four significant predictive factors of SHS in low-income older adults living alone were identified (42.5%): the number of chronic diseases, fear of falling, depression, and social support. Conclusion: SHS is a critical factor for older adults living alone on a low-income. Hence, evaluating SHS and developing interventions to improve it periodically is necessay. Such interventions should consider chronic disease management, screening and mediation for depression and fear of falling, and strengthening their social support systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1739-1748
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• 2021

Real estate prices affect countries, businesses, and households, and many studies have been conducted on the real estate bubble in recent soaring real estate prices. However, if the real estate bubble prediction simply compares the real estate price, or if it does not reflect key psychological variables in real estate sales, it can be judged that the accuracy of the bubble prediction model is poor. The purpose of this study is to design a predictive model that can explain the real estate bubble situation by region using the autoencoder technique. Existing real estate bubble analysis studies failed to set various types of variables that affect prices, and most of them were conducted based on linear models. Thus, this study suggests the possibility of introducing techniques and variables that have not been used in existing real estate bubble studies.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.127-142
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• 2023

The management of size and weight, which are the growth information of aquaculture fish in fish-farms, is the most basic goal. In this study, the epoch is defined in fish-farms from the time of stocking or dividing to the time of shipment, and the growth data for a total of three epoch is analyzed from a time series perspective. Growth information such as the size and weight of aquaculture fish that occur over time in fish-farms is compared and analyzed with water quality environmental information and feeding information, and a model is presented using the analysis results. In this study, linear, exponential, and logarithmic regression models are presented using the Box-Jenkins method for size and weight by epoch using data obtained in the field.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• Safety and Health at Work
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• v.13 no.4
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• pp.394-400
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• 2022

Background: Impacts of exposure are generally monitored and recorded after injuries or illness occur. Yet, absence of conventional after-the-effect impacts (i.e., lagging indicators), tend to focus on physical health and injuries, and fail to inform if workers are not exposed to safety and health hazards. In contrast to lagging indicators, leading indicators are proactive, preventive, and predictive indexes that offer insights how effective safety and health. The present study is to validate an extended Voluntary Protection Programs (VPP) that consists of six leading indicators. Methods: Questionnaires were distributed to 13 organizations (response rate = 93.1%, 1,439 responses) in Taiwan. Cronbach α, multiple linear regression and canonical correlation were used to test the reliability of the extended Voluntary Protection Programs (VPP) which consists of six leading indicators (safe climate, transformational leadership, organizational justice, organizational support, hazard prevention and control, and training). Criteria-related validation strategy was applied to examine relationships of six leading indicators with six criteria (perceived health, burnout, depression, job satisfaction, job performance, and life satisfaction). Results: The results showed that the Cronbach's α of six leading indicators ranged from 0.87 to 0.92. The canonical correlation analysis indicated a positive correlation between the six leading indicators and criteria (1st canonical function: correlation = 0.647, square correlation = 0.419, p < 0.001). Conclusions: The present study validates the extended VPP framework that focuses on promoting safety and physical and mental health. Results further provides applications of the extended VPP framework to promote workers' safety and health.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.1-10
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• 2023

This study investigated the relative effectiveness of letter-based versus syllable-based measures of speech rate and articulation rate in predicting the articulation score, prosody fluency, and rating sum using "English speech data of Koreans for education" from AI Hub. We extracted and analyzed 900 utterances from the training data, including three balanced age groups (13, 19, and 26 years old). The study built three models that best predicted the pronunciation assessment scores using linear mixed-effects regression and compared the predicted scores with the actual scores from the validation data (n=180). The correlation coefficients between them were also calculated. The findings revealed that syllable-based measures of speech and articulation rates were more effective than letter-based measures in all three pronunciation assessment categories. The correlation coefficients between the predicted and actual scores ranged from .65 to .68, indicating the models' good predictive power. However, it remains inconclusive whether speech rate or articulation rate is more effective.