• Journal of The Korean Society of Integrative Medicine
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• v.9 no.1
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• pp.203-217
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• 2021

Purpose: The purpose of this meta-analysis was to examine the high-level evidence of the effects of manual therapy on musculoskeletal diseases. Methods: Domestic databases were searched for studies that conducted clinical trials associated with manual therapy on chronic musculoskeletal diseases. A total of 591 studies published between 2005 and 2018 were identified, with 18 studies satisfying the inclusion data. The studies were classified according to patient, intervention, comparison, and outcome (PICO). The search outcomes were items associated with pain and physical function. The 18 studies included in the study were evaluated by using the R meta-analysis (version 4.0). The quality of 18 randomized control trials was evaluated by using the Cochrane risk of bias (ROB). The effect sizes were computed as the corrected standardized mean difference (SMD). Subgroup and meta-regression analyses were also used. Egger's regression test was carried out in order to analyze the publication bias. Cumulative meta-analysis and sensitivity analysis were also conducted in order to analyze the data error. Results: The following factors showed the large effect size of manual therapy on chronic musculoskeletal diseases: pain (Hedges's g = 2.66; 95% CI = 1.47 ~ 3.85), and physical function (Hedges's g = 2.15; 95% CI: 1.22 ~ 3.08). The subgroup analysis only showed a statistical difference in the type of manual therapy (pain) and outcome (physical function). No statistically significant difference was found in the meta-regression analysis. Publication bias was found in the data, but the results of the trim-and-fill method showed that such bias did not largely affect the obtained data. Furthermore, there were no data errors in the cumulative meta-analysis and sensitivity analysis. Conclusion: This study provides evidence for the effectiveness of manual therapy on chronic musculoskeletal diseases in pain and physical function. Subgroup analysis suggests that only the type of manual therapy for pain and the type of outcome for physical function differed in effect size.

• Journal of Korean Society on Water Environment
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• v.26 no.3
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• pp.482-490
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• 2010

In this study 17 GCMs' simulations of late 20th century climate in Korea are examined. A regionally averaged time series formed by averaging the temperature and precipitation values at all the Korean grid points. In order to compare general circulation models with observations, observed spatially averaged temperature and precipitation is calculated using 24 stations for 1971 to 2000. The annual mean difference between models and observed data are compared. For temperature, most models have a slight cold bias. The models with least bias in annual average temperature are NIES(MIROC3.2 hires), GISS(AOM) and INGV(SXG2005). For precipitation, almost all models have a dry bias, and for some the bias exceeds 50%. Models with lowest bias are NIES(MIROC3.2 hires), CCCma(CGCM3-T47) and MPI-M(ECHAM5-OM). The models' simulated seasonal cycles show that for temperature, CSIRO(Mk3.0) has the best followed by CCCma(CGCM3-T47) and CCCma(CGCM3-T63), and for precipitation, NIES(MIROC3.2 hires) has the best followed by CSIRO(Mk3.0) and CNRM(CM3). In the assessment using Taylor diagram, CCCma(CGCM3-T47) ranks the best for temperature, and NIES(MIROC3.2 hires) ranks the best for precipitation.

• Journal of The Korean Society of Integrative Medicine
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• v.8 no.4
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• pp.1-11
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• 2020

Purpose : The purpose of this meta-analysis was to examine the effects of microcurrent on inflammatory musculoskeletal diseases. Methods : Domestic databases (RISS, NDSL, KISS, DBpia, and Kmbase) were searched for studies that conducted clinical trials associated with microcurrent and its impact on inflammatory musculoskeletal diseases. A total of 606 studies published between 2002 and 2019 were identified, with 8 studies satisfying the inclusion data. The studies were classified according to patient, intervention, comparison, and outcome (PICO). The search outcomes were items associated with blood component, pain, and function. The 8 studies that were included in the study were evaluated using R meta-analysis (version 4.0). The quality of 7 randomized control trials was evaluated using Cochrane risk of bias (ROB). The quality of 1 non-randomized control trial was evaluated using risk of bias assessment tool for non-randomized studies (RoBANS). Effect sizes were computed as the corrected standard mean difference (SMD). A random-effect model was used to analyze the effect size because of the high heterogeneity among the studies. Egger's regression test was carried out to analyze the publishing bias. Results : The following factors had a large effect size involving microcurrent on inflammatory musculoskeletal diseases: blood component (Hedges's g=-2.46, 95 % CI=-4.20~-0.73), pain (Hedges's g=3.51, 95 % CI=2.44~4.77), and function (Hedges's g=3.06, 95 % CI: 1.53~4.58). Except for function (t=1.572, p=.191), Egger's regression test showed that the publishing bias had statistically significant differences. Conclusion : This study provides evidence for the effectiveness of microcurrent on inflammatory musculoskeletal diseases in terms of blood component, pain, and function. However, due to the small sample sizes used in the included studies, the results of our study should be interpreted cautiously, especially considering the publishing bias.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.2_2
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• pp.293-306
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• 2023

The purpose of this study is a descriptive research study to analyze the factors that affect Christians' COVID-19 preventive behavior. The subjects of the study were 262 adult Christians, and the data collected were using SPSS 25.0 and AMOS 21.0 programs. As a result of the study, the subject's religious maturity level was 4.21 ± .55 points out of 5 points, COVID-19 stress was 2.86 ± .73 points out of 5 points, optimism bias was 2.94 ± 1.26 points out of 7 points, and COVID-19 preventive health behavior was 4 points. The total score was 3. 54 ± . 44 points. As a result of the correlation analysis of the subject's religious maturity, COVID-19 stress, optimistic bias, and COVID-19 preventive health behaviors, COVID-19 preventive health behaviors were faith maturity (r=.156, p=.012), COVID-19 stress (r=.216, There was a positive correlation with optimism bias (r=174, p=.005). In conclusion, it can be said that the higher the religious maturity, the higher the COVID-19 stress, and the higher the optimistic bias, the better the preventive health behavior of COVID-19, and the explanatory power of the overall model was 9.4%. In the post-COVID-19 era, it is necessary to develop educational programs that can prevent infectious diseases and promote health in the community.

• Journal of the Korean Statistical Society
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• v.7 no.2
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• pp.101-104
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• 1978

This paper considers the problem of estimating $\hat{\beta}$ in the case errors occur in observing the values of q-variables $X_1, X_2, ..., X_q$. The approximated estimator $\hat{\beta}(e)$ is obtained and its expected value, bias and covariance matrix are studied.

• Journal of Korean Society of societal Security
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• v.2 no.3
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• pp.39-46
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• 2009

A small loop-loop multi-frequency electromagnetic(EM) induction method is a useful technique to map a resistivity distribution efficiently and non-destructively. However, for quantitative interpretation and depth sounding, the quality of measured data is crucial. In this paper, we propose a bias correction of measured data by using background noise measurements to obtain reliable data, and propose an evaluation technique of apparent that can provide a resistivity image easily. We have performed small loop-loop EM measurements to detect water saturation in a man-made test site. The application of our proposed techniques to the measured data was successful.

• Journal of Preventive Medicine and Public Health
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• v.50 no.5
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• pp.294-302
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• 2017

Objectives: The objectives of this study were to investigate the agreement between medical history questionnaire data and claims data and to identify the factors that were associated with discrepancies between these data types. Methods: Data from self-reported questionnaires that assessed an individual's history of hypertension, diabetes mellitus, dyslipidemia, stroke, heart disease, and pulmonary tuberculosis were collected from a general health screening database for 2014. Data for these diseases were collected from a healthcare utilization claims database between 2009 and 2014. Overall agreement, sensitivity, specificity, and kappa values were calculated. Multiple logistic regression analysis was performed to identify factors associated with discrepancies and was adjusted for age, gender, insurance type, insurance contribution, residential area, and comorbidities. Results: Agreement was highest between questionnaire data and claims data based on primary codes up to 1 year before the completion of self-reported questionnaires and was lowest for claims data based on primary and secondary codes up to 5 years before the completion of self-reported questionnaires. When comparing data based on primary codes up to 1 year before the completion of selfreported questionnaires, the overall agreement, sensitivity, specificity, and kappa values ranged from 93.2 to 98.8%, 26.2 to 84.3%, 95.7 to 99.6%, and 0.09 to 0.78, respectively. Agreement was excellent for hypertension and diabetes, fair to good for stroke and heart disease, and poor for pulmonary tuberculosis and dyslipidemia. Women, younger individuals, and employed individuals were most likely to under-report disease. Conclusions: Detailed patient characteristics that had an impact on information bias were identified through the differing levels of agreement.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.79-84
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• 2021

A feature extraction method capable of reflecting features well while mainaining the properties of data is required in order to process high-dimensional data. The principal component analysis method that converts high-level data into low-dimensional data and express high-dimensional data with fewer variables than the original data is a representative method for feature extraction of data. In this study, we propose a principal component analysis method based on adaptive correlation when selecting principal component variables in principal component analysis for data feature extraction when the data is high-dimensional. The proposed method analyzes the principal components of the data by adaptively reflecting the correlation based on the correlation between the input data. I want to exclude them from the candidate list. It is intended to analyze the principal component hierarchy by the eigen-vector coefficient value, to prevent the selection of the principal component with a low hierarchy, and to minimize the occurrence of data duplication inducing data bias through correlation analysis. Through this, we propose a method of selecting a well-presented principal component variable that represents the characteristics of actual data by reducing the influence of data bias when selecting the principal component variable.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.4107-4107
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• 2001

Previous works have shown the viability of NIRS technology for the prediction of fatty acids in Iberian pig fat, but although the resulting equations showed high precision, in the predictions of new samples important fluctuations were detected, greater with the time passed from calibration development to NIRS analysis. This fact makes the use of NIRS calibrations in routine analysis difficult. Moreover, this problem only appears in products like fat, that show spectrums with very defined absorption peaks at some wavelengths. This circumstance causes a high sensibility to small changes of the instrument, which are not perceived with the normal checks. To avoid these inconveniences, the software WinISI 1.04 has a mathematic algorithm that consist of create a “Repeatability File”. This file is used during calibration development to minimize the variation sources that can affect the NIRS predictions. The objective of the current work is the evaluation of the use of a repeatability file in quantitative NIRS analysis of Iberian pig fat. A total of 188 samples of Iberian pig fat, produced by COVAP, were used. NIR data were recorded using a FOSS NIRSystems 6500 I spectrophotometer equipped with a spinning module. Samples were analysed by folded transmission, using two sample cells of 0.1mm pathlength and gold surface. High accuracy calibration equations were obtained, without and with repeatability file, to determine the content of six fatty acids: miristic (SECV$\sub$without/=0.07% r$^2$$\sub$without/=0.76 and SECV$\sub$with/=0.08% r$^2$$\sub$with/=0.65), Palmitic (SECV$\sub$without/=0.28 r$^2$$\sub$without/=0.97 and SECV$\sub$with/=0.24% r$^2$$\sub$with/=0.98), palmitoleic (SECV$\sub$without/=0.08 r$^2$$\sub$without/=0.94 and SECV$\sub$with/=0.09% r$^2$$\sub$with/=0.92), Stearic (SECV$\sub$without/=0.27 r$^2$$\sub$without/=0.97 and SECV$\sub$with/=0.29% r$^2$$\sub$with/=0.96), oleic (SECV$\sub$without/=0.20 r$^2$$\sub$without/=0.99 and SECV$\sub$with/=0.20% r$^2$$\sub$with/=0.99) and linoleic (SECV$\sub$without/=0.16 r$^2$$\sub$without/=0.98 and SECV$\sub$with/=0.16% r$^2$$\sub$with/=0.98). The use of a repeatability file like a tool to reduce the variation sources that can disturbed the prediction accuracy was very effective. Although in calibration results the differences are negligible, the effect caused by the repeatability file is appreciated mainly when are predicted new samples that are not in the calibration set and whose spectrum were recorded a long time after the equation development. In this case, bias values corresponding to fatty acids predictions were lower when the repeatability file was used: miristic (bias$\sub$without/=-0.05 and bias$\sub$with/=-0.04), Palmitic (bias$\sub$without/=-0.42 and bias$\sub$with/=-0.11), Palmitoleic (bias$\sub$without/=-0.03 and bias$\sub$with/=0.03), Stearic (bias$\sub$without/=0.47 and bias$\sub$with/=0.28), oleic (bias$\sub$without/=0.14 and bias$\sub$with/=-0.04) and linoleic (bias$\sub$without/=0.25 and bias$\sub$with/=-0.20).

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.349-356
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• 2009

In this study, we suggest the estimation method of the relative risk for the unemployment statistics of a small area such as si, gun, gu in Korea. The considered method are the usual pooled estimator, weighted estimator with the inverse of log-variance as weights, and the Jackknife estimator. And we compare with the efficiency of the three estimators by estimating the bias and mean square errors using real data from the 2002 Economically Active Population Survey of Gyeonggi-do. We compute the unemployed rate of male and female in small areas, and then estimate the common relative risk for the unemployed rate between male and female. Also, the stability and reliability of the three estimators for the common relative risk was evaluated using the RB(relative bias) and the RRMSE(relative root mean square error) of these estimators. Finally, the Jackknife estimator turned out to be much more efficient than the other estimators.