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He received the DrSc degree in Biomedical Science from Ajou University School of Medicine. He is a professor in Department of Medical Big Data in Inje University. His recent interests focus on big data, machine learning, health promotion, gerontology, dementia, mild cognitive impairment, dysphonia.
This epidemiological study aimed to develop an X-AI that could explain groups with a high anxiety disorder risk in old age. To achieve this objective, (1) this study explored the predictors of senile anxiety using base models and meta models. (2) This study presented decision tree visualization that could help psychiatric consultants and primary physicians easily interpret the path of predicting high-risk groups based on major predictors derived from final machine learning models with the best performance. This study analyzed 1558 elderly (695 males and 863 females) who were 60 years or older and completed the Zung’s Self-Rating Anxiety Scale (SAS). We used support vector machine (SVM), random forest, LightGBM, and Adaboost for the base model, a single predictive model, while using XGBoost algorithm for the meta model. The analysis results confirmed that the predictive performance of the “SVM + Random forest + LightGBM + AdaBoost + XGBoost model (stacking ensemble: accuracy 87.4%, precision 85.1%, recall 87.4%, and F1-score 85.5%)” was the best. Also, the results of this study showed that the elderly who often (or mostly) felt subjective loneliness, had a Self Esteem Scale score of 26 or less, and had a subjective communication with their family of 4 or less (on a 10-point scale) were the group with the highest risk anxiety disorder. The results of this study imply that it is necessary to establish a community-based mental health policy that can identify elderly groups with high anxiety risks based on multiple risk factors and manage them constantly.
Haewon Byeon. Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study. International Journal of Environmental Research and Public Health 2021, 18, 7625 .
AMA StyleHaewon Byeon. Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study. International Journal of Environmental Research and Public Health. 2021; 18 (14):7625.
Chicago/Turabian StyleHaewon Byeon. 2021. "Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study." International Journal of Environmental Research and Public Health 18, no. 14: 7625.
This cross-sectional study developed a nomogram that could allow medical professionals in the primary care setting to easily and visually confirm high-risk groups of depression. This study analyzed 4011 elderly people (≥60 years old) who completed a health survey, blood pressure, physical measurement, blood test, and a standardized depression screening test. A major depressive disorder was measured using the Korean version of the Patient Health Questionnaire (PHQ-9). This study built a model for predicting major depressive disorders using logistic regression analysis to understand the relationship of each variable with major depressive disorders. In the result, the prevalence of depression measured by PHQ-9 was 6.8%. The results of multiple logistic regression analysis revealed that the major depressive disorder of the elderly living alone was significantly (p< 0.05) related to monthly mean household income, the mean frequency of having breakfast per week for the past year, moderate-intensity physical activity, subjective level of stress awareness, and subjective health status. The results of this study implied that it would be necessary to continuously monitor these complex risk factors such as household income, skipping breakfast, moderate-intensity physical activity, subjective stress, and subjective health status to prevent depression among older adults living in the community.
Haewon Byeon. Development of a Nomogram for Predicting Depression in the Elderly Using Patient Health Questionnaire-9 among a Nationwide Sample of Korean Elderly. Journal of Personalized Medicine 2021, 11, 645 .
AMA StyleHaewon Byeon. Development of a Nomogram for Predicting Depression in the Elderly Using Patient Health Questionnaire-9 among a Nationwide Sample of Korean Elderly. Journal of Personalized Medicine. 2021; 11 (7):645.
Chicago/Turabian StyleHaewon Byeon. 2021. "Development of a Nomogram for Predicting Depression in the Elderly Using Patient Health Questionnaire-9 among a Nationwide Sample of Korean Elderly." Journal of Personalized Medicine 11, no. 7: 645.
In this study, we measured the convergence rate using the mean-squared error (MSE) of the standardized neuropsychological test to determine the severity of Parkinson’s disease dementia (PDD), which is based on support vector machine (SVM) regression (SVR) and present baseline data in order to develop a model to predict the severity of PDD. We analyzed 328 individuals with PDD who were 60 years or older. To identify the SVR with the best prediction power, we compared the classification performance (convergence rate) of eight SVR models (Eps-SVR and Nu-SVR with four kernel functions (a radial basis function (RBF), linear algorithm, polynomial algorithm, and sigmoid)). Among the eight models, the MSE of Nu-SVR-RBF was the lowest (0.078), with the highest convergence rate, whereas the MSE of Eps-SVR-sigmoid was 0.110, with the lowest convergence rate. The results of this study imply that this approach could be useful for measuring the severity of dementia by comprehensively examining axial atypical features, the Korean instrumental activities of daily living (K-IADL), changes in rapid eye movement sleep behavior disorder (RBD), etc. for optimal intervention and caring of the elderly living alone or patients with PDD residing in medically vulnerable areas.
Haewon Byeon. Predicting the Severity of Parkinson’s Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model. International Journal of Environmental Research and Public Health 2021, 18, 2551 .
AMA StyleHaewon Byeon. Predicting the Severity of Parkinson’s Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model. International Journal of Environmental Research and Public Health. 2021; 18 (5):2551.
Chicago/Turabian StyleHaewon Byeon. 2021. "Predicting the Severity of Parkinson’s Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model." International Journal of Environmental Research and Public Health 18, no. 5: 2551.
It is essential to understand the voice characteristics in the normal aging process to accurately distinguish presbyphonia from neurological voice disorders. This study developed the best ensemble-based machine learning classifier that could distinguish hypokinetic dysarthria from presbyphonia using classification and regression tree (CART), random forest, gradient boosting algorithm (GBM), and XGBoost and compared the prediction performance of models. The subjects of this study were 76 elderly patients diagnosed with hypokinetic dysarthria and 174 patients with presbyopia. This study developed prediction models for distinguishing hypokinetic dysarthria from presbyphonia by using CART, GBM, XGBoost, and random forest and compared the accuracy, sensitivity, and specificity of the development models to identify the prediction performance of them. The results of this study showed that random forest had the best prediction performance when it was tested with the test dataset (accuracy = 0.83, sensitivity = 0.90, and specificity = 0.80, and area under the curve (AUC) = 0.85). The main predictors for detecting hypokinetic dysarthria were Cepstral peak prominence (CPP), jitter, shimmer, L/H ratio, L/H ratio_SD, CPP max (dB), CPP min (dB), and CPPF0 in the order of magnitude. Among them, CPP was the most important predictor for identifying hypokinetic dysarthria.
Haewon Byeon. Comparing Ensemble-Based Machine Learning Classifiers Developed for Distinguishing Hypokinetic Dysarthria from Presbyphonia. Applied Sciences 2021, 11, 2235 .
AMA StyleHaewon Byeon. Comparing Ensemble-Based Machine Learning Classifiers Developed for Distinguishing Hypokinetic Dysarthria from Presbyphonia. Applied Sciences. 2021; 11 (5):2235.
Chicago/Turabian StyleHaewon Byeon. 2021. "Comparing Ensemble-Based Machine Learning Classifiers Developed for Distinguishing Hypokinetic Dysarthria from Presbyphonia." Applied Sciences 11, no. 5: 2235.
Despite the frequent progression from Parkinson’s disease (PD) to Parkinson’s disease dementia (PDD), the basis to diagnose early-onset Parkinson dementia (EOPD) in the early stage is still insufficient. To explore the prediction accuracy of sociodemographic factors, Parkinson's motor symptoms, Parkinson’s non-motor symptoms, and rapid eye movement sleep disorder for diagnosing EOPD using PD multicenter registry data. This study analyzed 342 Parkinson patients (66 EOPD patients and 276 PD patients with normal cognition), younger than 65 years. An EOPD prediction model was developed using a random forest algorithm and the accuracy of the developed model was compared with the naive Bayesian model and discriminant analysis. The overall accuracy of the random forest was 89.5%, and was higher than that of discriminant analysis (78.3%) and that of the naive Bayesian model (85.8%). In the random forest model, the Korean Mini Mental State Examination (K-MMSE) score, Korean Montreal Cognitive Assessment (K-MoCA), sum of boxes in Clinical Dementia Rating (CDR), global score of CDR, motor score of Untitled Parkinson’s Disease Rating (UPDRS), and Korean Instrumental Activities of Daily Living (K-IADL) score were confirmed as the major variables with high weight for EOPD prediction. Among them, the K-MMSE score was the most important factor in the final model. It was found that Parkinson-related motor symptoms (e.g., motor score of UPDRS) and instrumental daily performance (e.g., K-IADL score) in addition to cognitive screening indicators (e.g., K-MMSE score and K-MoCA score) were predictors with high accuracy in EOPD prediction.
Haewon Byeon. Best early-onset Parkinson dementia predictor using ensemble learning among Parkinson's symptoms, rapid eye movement sleep disorder, and neuropsychological profile. World Journal of Psychiatry 2020, 10, 245 -259.
AMA StyleHaewon Byeon. Best early-onset Parkinson dementia predictor using ensemble learning among Parkinson's symptoms, rapid eye movement sleep disorder, and neuropsychological profile. World Journal of Psychiatry. 2020; 10 (11):245-259.
Chicago/Turabian StyleHaewon Byeon. 2020. "Best early-onset Parkinson dementia predictor using ensemble learning among Parkinson's symptoms, rapid eye movement sleep disorder, and neuropsychological profile." World Journal of Psychiatry 10, no. 11: 245-259.
It is important to diagnose depression in Parkinson’s disease (DPD) as soon as possible and identify the predictors of depression to improve quality of life in Parkinson’s disease (PD) patients. To develop a model for predicting DPD based on the support vector machine, while considering sociodemographic factors, health habits, Parkinson's symptoms, sleep behavior disorders, and neuropsychiatric indicators as predictors and provide baseline data for identifying DPD. This study analyzed 223 of 335 patients who were 60 years or older with PD. Depression was measured using the 30 items of the Geriatric Depression Scale, and the explanatory variables included PD-related motor signs, rapid eye movement sleep behavior disorders, and neuropsychological tests. The support vector machine was used to develop a DPD prediction model. When the effects of PD motor symptoms were compared using “functional weight”, late motor complications (occurrence of levodopa-induced dyskinesia) were the most influential risk factors for Parkinson's symptoms. It is necessary to develop customized screening tests that can detect DPD in the early stage and continuously monitor high-risk groups based on the factors related to DPD derived from this predictive model in order to maintain the emotional health of PD patients.
Haewon Byeon. Development of a depression in Parkinson's disease prediction model using machine learning. World Journal of Psychiatry 2020, 10, 234 -244.
AMA StyleHaewon Byeon. Development of a depression in Parkinson's disease prediction model using machine learning. World Journal of Psychiatry. 2020; 10 (10):234-244.
Chicago/Turabian StyleHaewon Byeon. 2020. "Development of a depression in Parkinson's disease prediction model using machine learning." World Journal of Psychiatry 10, no. 10: 234-244.
The rapid eye movement sleep behavior disorder (RBD) of Parkinson’s disease (PD) patients can be improved with medications such as donepezil as long as it is diagnosed with a thorough medical examination, since identifying a high-risk group of RBD is a critical issue to treat PD. This study develops a model for predicting the high-risk groups of RBD using random forest (RF) and provides baseline information for selecting subjects for polysomnography. Subjects consisted of 350 PD patients (Parkinson’s disease with normal cognition (PD-NC) = 48; Parkinson’s disease with mild cognitive impairment (PD-MCI) = 199; Parkinson’s disease dementia (PDD) = 103) aged 60 years and older. This study compares the prediction performance of RF, discriminant analysis, classification and regression tree (CART), radial basis function (RBF) neural network, and logistic regression model to select a final model with the best model performance and presents the variable importance of the final model’s variable. As a result of analysis, the sensitivity of RF (79%) was superior to other models (discriminant analysis = 14%, CART = 32%, RBF neural network = 25%, and logistic regression = 51%). It was confirmed that age, the motor score of Untitled Parkinson’s Disease Rating (UPDRS), the total score of UPDRS, the age when a subject was diagnosed with PD first time, the Korean Mini Mental State Examination, and Korean Instrumental Activities of Daily Living, were major variables with high weight for predicting RBD. Among them, age was the most important factor. The model for predicting Parkinson’s disease RBD developed in this study will contribute to the screening of patients who should receive a video-polysomnography.
Haewon Byeon. Exploring the Predictors of Rapid Eye Movement Sleep Behavior Disorder for Parkinson’s Disease Patients Using Classifier Ensemble. Healthcare 2020, 8, 121 .
AMA StyleHaewon Byeon. Exploring the Predictors of Rapid Eye Movement Sleep Behavior Disorder for Parkinson’s Disease Patients Using Classifier Ensemble. Healthcare. 2020; 8 (2):121.
Chicago/Turabian StyleHaewon Byeon. 2020. "Exploring the Predictors of Rapid Eye Movement Sleep Behavior Disorder for Parkinson’s Disease Patients Using Classifier Ensemble." Healthcare 8, no. 2: 121.
In order to develop a predictive model that can distinguish Parkinson’s disease dementia (PDD) from other dementia types, such as Alzheimer’s dementia (AD), it is necessary to evaluate and identify the predictive accuracy of the cognitive profile while considering the non-motor symptoms, such as depression and rapid eye movement (REM) sleep behavior disorders. This study compared Parkinson’s disease (PD)’s non-motor symptoms and the diagnostic predictive power of cognitive profiles that distinguish AD and PD using machine learning. This study analyzed 118 patients with AD and 110 patients with PDD, and all subjects were 60 years or older. In order to develop the PDD prediction model, the dataset was divided into training data (70%) and test data (30%). The prediction accuracy of the model was calculated by the recognition rate. The results of this study show that Parkinson-related non-motor symptoms, such as REM sleep behavior disorders, and cognitive screening tests, such as Korean version of Montreal Cognitive Assessment, were highly accurate factors for predicting PDD. It is required to develop customized screening tests that can detect PDD in the early stage based on these results. Furthermore, it is believed that including biomarkers such as brain images or cerebrospinal fluid as input variables will be more useful for developing PDD prediction models in the future.
Haewon Byeon. Application of Machine Learning Technique to Distinguish Parkinson’s Disease Dementia and Alzheimer’s Dementia: Predictive Power of Parkinson’s Disease-Related Non-Motor Symptoms and Neuropsychological Profile. Journal of Personalized Medicine 2020, 10, 1 .
AMA StyleHaewon Byeon. Application of Machine Learning Technique to Distinguish Parkinson’s Disease Dementia and Alzheimer’s Dementia: Predictive Power of Parkinson’s Disease-Related Non-Motor Symptoms and Neuropsychological Profile. Journal of Personalized Medicine. 2020; 10 (2):1.
Chicago/Turabian StyleHaewon Byeon. 2020. "Application of Machine Learning Technique to Distinguish Parkinson’s Disease Dementia and Alzheimer’s Dementia: Predictive Power of Parkinson’s Disease-Related Non-Motor Symptoms and Neuropsychological Profile." Journal of Personalized Medicine 10, no. 2: 1.
Because it is possible to delay the progression of dementia if it is detected and treated in an early stage, identifying mild cognitive impairment (MCI) is an important primary goal of dementia treatment. The objectives of this study were to develop a random forest-based Parkinson’s disease with mild cognitive impairment (PD-MCI) prediction model considering health behaviors, environmental factors, medical history, physical functions, depression, and cognitive functions using the Parkinson’s Dementia Clinical Epidemiology Data (a national survey conducted by the Korea Centers for Disease Control and Prevention) and to compare the prediction accuracy of our model with those of decision tree and multiple logistic regression models. We analyzed 96 subjects (PD-MCI = 45; Parkinson’s disease with normal cognition (PD-NC) = 51 subjects). The prediction accuracy of the model was calculated using the overall accuracy, sensitivity, and specificity. Based on the random forest analysis, the major risk factors of PD-MCI were, in descending order of magnitude, Clinical Dementia Rating (CDR) sum of boxes, Untitled Parkinson’s Disease Rating (UPDRS) motor score, the Korean Mini Mental State Examination (K-MMSE) total score, and the K- Korean Montreal Cognitive Assessment (K-MoCA) total score. The random forest method achieved a higher sensitivity than the decision tree model. Thus, it is advisable to develop a protocol to easily identify early stage PDD based on the PD-MCI prediction model developed in this study, in order to establish individualized monitoring to track high-risk groups.
Haewon Byeon. Is the Random Forest Algorithm Suitable for Predicting Parkinson’s Disease with Mild Cognitive Impairment out of Parkinson’s Disease with Normal Cognition? International Journal of Environmental Research and Public Health 2020, 17, 2594 .
AMA StyleHaewon Byeon. Is the Random Forest Algorithm Suitable for Predicting Parkinson’s Disease with Mild Cognitive Impairment out of Parkinson’s Disease with Normal Cognition? International Journal of Environmental Research and Public Health. 2020; 17 (7):2594.
Chicago/Turabian StyleHaewon Byeon. 2020. "Is the Random Forest Algorithm Suitable for Predicting Parkinson’s Disease with Mild Cognitive Impairment out of Parkinson’s Disease with Normal Cognition?" International Journal of Environmental Research and Public Health 17, no. 7: 2594.
The objectives of this study were to identify the effects of smoking on the voice of smokers and present the baseline data for establishing the basis for preventing voice disorders. This study was evaluated using a meta-analysis from studies published between Jan 1, 2000, and Nov 15, 2018. As a result, the final meta-analysis was conducted using nine papers. The standard mean difference was analyzed after dividing the effects of smoking on voice into the pitch (F0), sound quality (jitter, shimmer, and noise to harmonic ratio; NHR), Maximum Phonation Time (MPT), and subjective voice problem. The results showed that there was a significant difference in F0 and MPT. On the other hand, the jitter, shimmer, NHR, and Voice Handicap Index (VHI) had different mean effect size but they were not significantly different. The analysis by sub-function of VHI results showed that the mean effect size was significantly different only in VHI-P (Physical). This study evaluated the effects of smoking on voice using meta-analysis. It was confirmed that smoking had significant and moderate effects on the F0 of voice, MPT, VHI, and physical functions. It is necessary for future meta-analysis studies to conduct randomized controlled experiments or longitudinal studies to confirm the effect sizes of variables.
Haewon Byeon; Seulki Cha. Evaluating the effects of smoking on the voice and subjective voice problems using a meta-analysis approach. Scientific Reports 2020, 10, 4720 -8.
AMA StyleHaewon Byeon, Seulki Cha. Evaluating the effects of smoking on the voice and subjective voice problems using a meta-analysis approach. Scientific Reports. 2020; 10 (1):4720-8.
Chicago/Turabian StyleHaewon Byeon; Seulki Cha. 2020. "Evaluating the effects of smoking on the voice and subjective voice problems using a meta-analysis approach." Scientific Reports 10, no. 1: 4720-8.
Purpose: This study aimed to conduct a qualitative evaluation by synthesizing previous studies on the effect of transcranial direct current stimulation (tDCS) on primary progressive aphasia (PPA)’s naming ability and prove the effects of tDCS mediation on PPA naming using meta-analysis. Methods: This study searched literature published from January 2000 to July 2019 using four academic databases (i.e., PubMed, Web of Science, MEDLINE, and Cochrane Library). The final seven publications were systematically evaluated and meta-analysis was conducted for two papers. The effect size was estimated by a standard mean difference (SMD) using Hedge’s g, and the significance of effect size was confirmed using the 95% confidence interval. Results: The results of seven previous studies’ quality assessments ranged from 15 to 26, which were rated above adequate. The results of the meta-analysis showed that the effect size was 0.82 (95% CI: 0.16–1.47), which was a significant ‘large effect’. Conclusions: This meta-analysis proved that tDCS intervention significantly improved the naming performance of PPA. Future studies must confirm the effects of tDCS on naming intervention by using meta-analysis including many RCT studies.
Haewon Byeon. Meta-Analysis on the Effects of Transcranial Direct Current Stimulation on Naming of Elderly with Primary Progressive Aphasia. International Journal of Environmental Research and Public Health 2020, 17, 1095 .
AMA StyleHaewon Byeon. Meta-Analysis on the Effects of Transcranial Direct Current Stimulation on Naming of Elderly with Primary Progressive Aphasia. International Journal of Environmental Research and Public Health. 2020; 17 (3):1095.
Chicago/Turabian StyleHaewon Byeon. 2020. "Meta-Analysis on the Effects of Transcranial Direct Current Stimulation on Naming of Elderly with Primary Progressive Aphasia." International Journal of Environmental Research and Public Health 17, no. 3: 1095.
It is necessary to identify how to improve the swallowing-related quality of life, as well as the swallowing function, in order to evaluate the effect of treatments on swallowing disorders. This study aimed to prove the effects of a compound swallowing intervention (Mendelsohn maneuver + neuromuscular electrical stimulation (NMES)) on the swallowing function and the quality of life by applying the compound swallowing intervention to patients with sub-acute swallowing disorders due to cerebral infarction for eight weeks. This study analyzed 43 subjects who were diagnosed with swallowing disorders due to cerebral infarction. The experiment consisted of the Mendelsohn maneuver treatment group (n = 15), the NMES treatment group (n = 13), the compound intervention group (Mendelsohn maneuver + NMES; n = 15). The results of ANCOVA showed that the changes in Functional Dysphagia Scale (FDS) scores and Swallowing–Quality of Life (SWAL–QOL) score were different among groups. The compound intervention group had the highest FDS scores and SWAL–QOL score followed by Mendelsohn, and the NMES group had the lowest. The result of this study suggests that NMES can be more effective when it is combined with a traditional swallowing rehabilitation therapy rather than a single intervention method.
Haewon Byeon. Combined Effects of NMES and Mendelsohn Maneuver on the Swallowing Function and Swallowing–Quality of Life of Patients with Stroke-Induced Sub-Acute Swallowing Disorders. Biomedicines 2020, 8, 12 .
AMA StyleHaewon Byeon. Combined Effects of NMES and Mendelsohn Maneuver on the Swallowing Function and Swallowing–Quality of Life of Patients with Stroke-Induced Sub-Acute Swallowing Disorders. Biomedicines. 2020; 8 (1):12.
Chicago/Turabian StyleHaewon Byeon. 2020. "Combined Effects of NMES and Mendelsohn Maneuver on the Swallowing Function and Swallowing–Quality of Life of Patients with Stroke-Induced Sub-Acute Swallowing Disorders." Biomedicines 8, no. 1: 12.
Background and Objectives: This study developed a support vector machine (SVM) algorithm-based prediction model with considering influence factors associated with the swallowing quality-of-life as the predictor variables and provided baseline information for enhancing the swallowing quality of elderly people’s lives in the future. Methods and Material: This study sampled 142 elderly people equal to or older than 65 years old who were using a senior welfare center. The swallowing problem associated quality of life was defined by the swallowing quality-of-life (SWAL-QOL). In order to verify the predictive power of the model, this study compared the predictive power of the Gaussian function with that of a linear algorithm, polynomial algorithm, and a sigmoid algorithm. Results: A total of 33.9% of the subjects decreased in swallowing quality-of-life. The swallowing quality-of-life prediction model for the elderly, based on the SVM, showed both preventive factors and risk factors. Risk factors were denture use, experience of using aspiration in the past one month, being economically inactive, having a mean monthly household income
Haewon Byeon. Predicting the Swallow-Related Quality of Life of the Elderly Living in a Local Community Using Support Vector Machine. International Journal of Environmental Research and Public Health 2019, 16, 4269 .
AMA StyleHaewon Byeon. Predicting the Swallow-Related Quality of Life of the Elderly Living in a Local Community Using Support Vector Machine. International Journal of Environmental Research and Public Health. 2019; 16 (21):4269.
Chicago/Turabian StyleHaewon Byeon. 2019. "Predicting the Swallow-Related Quality of Life of the Elderly Living in a Local Community Using Support Vector Machine." International Journal of Environmental Research and Public Health 16, no. 21: 4269.
Background and objectives: Only a few studies analyzed the physical activity level of elderly people living alone in local communities and evaluated the relationship between it and mental health. The purpose of this study was to investigate the relationship between regular physical activity and depression in the elderly living alone and to provide basic data for the prevention of depression in the elderly. Materials and Methods: We analyzed 256 elderly people living alone aged 65 years or older who completed the 2014 Korea National Health and Nutrition Examination Survey. Depression was defined as a score of 10 or higher using Patient Health Questionnaire-9 (PHQ-9). This study investigated walking per week, days of muscular strength exercise performance in the past 1 week, days of flexibility exercise in the past 1 week, mean hours in a sitting position per day, the numbers of days and hours conducting a high intensity physical activity in the past 1 week, and numbers of days and hours conducting a medium intensity physical activity in the past 1 week to define physical activity. Our study presented prevalence odds ratios (pOR) and 95% confidence interval (CI) by using complex sample logistic regression analysis in order to identify the relationship between physical activity and depression. Results: The results of complex sample logistic regression analysis showed that flexibility exercise was significantly related to depression (p < 0.05). On the other hand, the mean hours in a sitting position per day, aerobic physical activity, walking, and muscular strength exercise were not significantly related to geriatric depression. Conclusions: The results of our study implied that persistent flexibility exercise might be more effective to maintain a healthy mental status than muscular strength exercise. A longitudinal study is required to prove the causal relationship between physical activity and depression in the old age.
Haewon Byeon. Relationship between Physical Activity Level and Depression of Elderly People Living Alone. International Journal of Environmental Research and Public Health 2019, 16, 4051 .
AMA StyleHaewon Byeon. Relationship between Physical Activity Level and Depression of Elderly People Living Alone. International Journal of Environmental Research and Public Health. 2019; 16 (20):4051.
Chicago/Turabian StyleHaewon Byeon. 2019. "Relationship between Physical Activity Level and Depression of Elderly People Living Alone." International Journal of Environmental Research and Public Health 16, no. 20: 4051.
Background and Objectives: Identifying the risk factors of teachers' voice disorders is very important for preventing voice disorders and the recurrence of them. This meta-study identified risk factors associated with teachers' voice disorders through systematic review and meta-analysis and provided basic data for preventing them. Materials and Methods: This study collected literature on the risk factors of teachers' voice disorders using six databases (i.e., CINAHL, EBSCO, PUBMED, SCOPUS, Web of Science, and Springer Link). Search was limited to studies published between 1 January 2000 and 15 October 2018, and a total of 16 publications were selected for the analysis of this study. The quality of selected literature was assessed using the "Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields". The effect size was analyzed by odds ratio and 95% confidence interval. Results: The results of the quality assessment ranged from 20 to 24 points with six strong studies and ten good studies. The meta-analysis showed that gender, upper airway problems, caffeine consumption, speaking loudly, number of classes per week, and resignation experience due to voice problems were the major risk factors of teachers' voice disorders. On the other hand, age, number of children, drinking, physical activity, smoking, water intake, singing habits, duration of teaching, perception of noise inside the school, number of classes per day, noise assessment inside the classroom, and perception of technology and instruments inside the workplace were not significantly related to voice disorders. Conclusions: Longitudinal studies should be conducted in the future to confirm causality between voice disorders and risk factors based on the results of this study.
Haewon Byeon. The Risk Factors Related to Voice Disorder in Teachers: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health 2019, 16, 3675 .
AMA StyleHaewon Byeon. The Risk Factors Related to Voice Disorder in Teachers: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health. 2019; 16 (19):3675.
Chicago/Turabian StyleHaewon Byeon. 2019. "The Risk Factors Related to Voice Disorder in Teachers: A Systematic Review and Meta-Analysis." International Journal of Environmental Research and Public Health 16, no. 19: 3675.
Although many studies have reported that allergic rhinitis is an independent risk factor highly related to otitis media in children, there is still lack of epidemiological studies on demographics. The objective of this study was to identify if allergic rhinitis was an independent risk factor for otitis media in children aged between 7 and 12 years by using the nationwide survey data representing the local population of South Korea. This is a secondary study based on the ENT examination data (eg. acute otitis media, otitis media with effusion, chronic otitis media). The subjects of this study were 472 children (248 male and 224 female) who completed the Korea National Health and Nutrition Examination Survey 2015. The presence of otitis media was examined by otolaryngologists using tympanometric measurements, audiometric measurements, and otoscopic examination. Allergic rhinitis was diagnosed by Korean-version of International Study of Asthma and Allergies in Childhood, a total serum immunoglobulin E test, an allergen-specific immunoglobulin E test, a blood eosinophil test, an eosinophil cationic protein test, a nasal cytology for eosinophils test, a skin reaction test, and an antigen simultaneous test. Confounding factors included age, gender, the levels of income for households, and household composition. The relationship between allergic rhinitis and otitis media was analyzed by a complex sample logistic regression analysis and the odds ratio and 95% confidence interval were presented. The results of a complex sample design logistic regression revealed that allergic rhinitis in children was significantly associated with otitis media (p < 0.05). Even after adjusting all confounding factors, children with allergic rhinitis had twice significantly higher risk of otitis (OR = 2.04; 95% CI: 1.30–3.18) than children without allergic rhinitis. This epidemiologic study confirmed the independent relationship between pediatric allergic rhinitis and otitis media. In the future, longitudinal study will be needed to verify causality of allergic rhinitis and otitis media.
Haewon Byeon. The association between allergic rhinitis and otitis media: A national representative sample of in South Korean children. Scientific Reports 2019, 9, 1 -7.
AMA StyleHaewon Byeon. The association between allergic rhinitis and otitis media: A national representative sample of in South Korean children. Scientific Reports. 2019; 9 (1):1-7.
Chicago/Turabian StyleHaewon Byeon. 2019. "The association between allergic rhinitis and otitis media: A national representative sample of in South Korean children." Scientific Reports 9, no. 1: 1-7.
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Haewon Byeon; Yu SeongHun; SungHyoun Cho. Level of knowledge of the elderly in local communities on the swallowing disorders after stroke and related factors. Indian Journal of Public Health Research & Development 2018, 9, 387 .
AMA StyleHaewon Byeon, Yu SeongHun, SungHyoun Cho. Level of knowledge of the elderly in local communities on the swallowing disorders after stroke and related factors. Indian Journal of Public Health Research & Development. 2018; 9 (8):387.
Chicago/Turabian StyleHaewon Byeon; Yu SeongHun; SungHyoun Cho. 2018. "Level of knowledge of the elderly in local communities on the swallowing disorders after stroke and related factors." Indian Journal of Public Health Research & Development 9, no. 8: 387.
Haewon Byeon; Heekyung Jin; SungHyoun Cho. Development of Parkinson's Disease Dementia Prediction Model Based on Verbal Memory, Visuospatial Memory, and Executive Function. Journal of Medical Imaging and Health Informatics 2017, 7, 1517 -1521.
AMA StyleHaewon Byeon, Heekyung Jin, SungHyoun Cho. Development of Parkinson's Disease Dementia Prediction Model Based on Verbal Memory, Visuospatial Memory, and Executive Function. Journal of Medical Imaging and Health Informatics. 2017; 7 (7):1517-1521.
Chicago/Turabian StyleHaewon Byeon; Heekyung Jin; SungHyoun Cho. 2017. "Development of Parkinson's Disease Dementia Prediction Model Based on Verbal Memory, Visuospatial Memory, and Executive Function." Journal of Medical Imaging and Health Informatics 7, no. 7: 1517-1521.
Objectives There is a possibility of underestimation in the smoking rate surveyed by self-reported questionnaires. This study investigated the difference between the Korean female smoking rate as determined by self-reports and that determined by a biochemical test and elucidated the relationship between women's smoking and laryngeal disorders. Design Nationwide cross-sectional survey. Setting 2008 Korea National Health and Nutrition Examination Survey. Participants 1849 women who completed the health survey, urinary cotinine test and laryngoscope examinations. Main outcome measure This study defined smokers as those with urine cotinine contents of 50 ng/mL and over. Confounding factors included age, level of education, household income, occupation and problem drinking in the past year. For statistical tests, OR and 95% CI were presented by using complex samples logistic regression. Results While there was no relationship between smoking as determined by a self-reported questionnaire and laryngeal disorders, smoking as determined by the urine cotinine test had a significant relationship with laryngeal disorders (p<0.05). After all the confounding factors were adjusted, those with urine cotinine concentrations of over 50 ng/mL had a 2.1 times higher risk of laryngeal disorders than those with urine cotinine concentrations of <50 ng/mL (OR=2.05, 95% CI 1.11 to 3.78) (p<0.05). Conclusions This national cross-sectional study verified that smoking is a significant risk factor for laryngeal disorders. Longitudinal studies are required to identify the causal relationship between smoking and laryngeal disorders.
Haewon Byeon; Dongwoo Lee; SungHyoun Cho. Relationship between women's smoking and laryngeal disorders based on the urine cotinine test: results of a national population-based survey. BMJ Open 2016, 6, e012169 .
AMA StyleHaewon Byeon, Dongwoo Lee, SungHyoun Cho. Relationship between women's smoking and laryngeal disorders based on the urine cotinine test: results of a national population-based survey. BMJ Open. 2016; 6 (11):e012169.
Chicago/Turabian StyleHaewon Byeon; Dongwoo Lee; SungHyoun Cho. 2016. "Relationship between women's smoking and laryngeal disorders based on the urine cotinine test: results of a national population-based survey." BMJ Open 6, no. 11: e012169.
The relationship between second-hand smoking and laryngopathy has not yet been reported. Thus, this study investigates the relationship between second-hand smoking and laryngopathy and suggests basic empirical data to prevent laryngopathy. This study analyzed 1,905 non-smokers over the age of 19 (269 men and 1,636 women) who completed the health questionnaire, laryngeal endoscope test, and urine cotinine test in the 2008 Korea National Health and Nutrition Examination Survey (KNHANES). Second-hand smoking was defined as a urine cotinine concentration of 50ng/ml and over. Confounding factors included age, gender, education, household income, occupation, alcohol consumption, and coffee consumption. Risk ratios (RR) and 95% confidence intervals (CI) were presented for the relationship between second-hand smoking and laryngopathy by using Poisson regression analysis. There was a significant relationship between second-hand smoking and laryngopathy (p<0.05). After all compounding factors were adjusted, non-smokers exposed to second-hand smoking had a 2.5 times (RR = 2.47, 95% CI: 1.19–5.08) significantly higher risk of laryngopathy than non-smokers not exposed to second-hand smoking (p<0.05). In this epidemiological study, there was a significant relationship between second-hand smoking and laryngopathy. More effective anti-smoking policies are required to protect the health of both non-smokers and smokers.
Haewon Byeon; Dongwoo Lee; SungHyoun Cho. Association between Second-Hand Smoking and Laryngopathy in the General Population of South Korea. PLOS ONE 2016, 11, e0165337 .
AMA StyleHaewon Byeon, Dongwoo Lee, SungHyoun Cho. Association between Second-Hand Smoking and Laryngopathy in the General Population of South Korea. PLOS ONE. 2016; 11 (11):e0165337.
Chicago/Turabian StyleHaewon Byeon; Dongwoo Lee; SungHyoun Cho. 2016. "Association between Second-Hand Smoking and Laryngopathy in the General Population of South Korea." PLOS ONE 11, no. 11: e0165337.