• Journal of Korean Academy of Nursing
• /
• v.50 no.6
• /
• pp.799-812
• /
• 2020

Purpose: This study aimed to construct and test a predictive model that explains and predicts the quality of life in older men living alone. Methods: A self-report questionnaire was used to collect data from 334 older adult men living along aged 65 years or over living in Jeollanam-do provinces. The endogenous variables were depression, self-rated health, instrumental activity of daily life, health promotion behaviors, the number of social participation activities and quality of life. Data were analyzed using the SPSS 21.0 and AMOS 21.0 programs. Results: The final model with 14 of the 8 analysed paths showed a good fit to the empirical data: χ2 = 173.26(p < .001, df = 53), normed χ2 = 3.27, GFI = .92, NFI = .90, CFI = .93, TLI = .89, RMSEA = .08 and SRMR = .06. Activities had direct effect on quality of life of older men living alone and social support had both direct and indirect effects. Meanwhile, function and socioeconomic status showed only indirect effects. The variables included in the eight significant paths explained 83.7% of variance in the prediction model. Conclusion: Instrumental activities of daily living and social support effect directly on quality of life in the older men living alone. Findings suggest that health care providers including community nurses need to provide social support as well as empowerment programs of instrumental activities of daily living and health promotion for improving quality of life of the older men living alone.

• Journal of Korean Society for Atmospheric Environment
• /
• v.25 no.4
• /
• pp.339-346
• /
• 2009

The objectives of this study were to introduce spatial interpolation methods which have been applied in recent papers, to apply three methods (nearest monitor, inverse distance weighting, kriging) to domestic data (Ulsan cohort) as an example of estimating the personal exposure levels. We predicted the personal exposure estimates of 2,102 participants in Ulsan cohort using spatial interpolation methods based on information of their residential address. We found that there was a similar tendency among the estimates of each method. The correlation coefficients between predictions from pairs of interpolation methods (except for the correlation coefficient between nearest montitor and kriging of CO and $SO_2$) were generally high (r=0.84 to 0.96). Even if there are some limitations such as location and density of monitoring station, spatial interpolation methods can reflect spatial aspects of air pollutant and spatial heterogeneity in individual level so that they provide more accurate estimates than monitor data alone. But they may still result in misclassification of exposure. To minimize misclassification for better estimates, we need to consider individual characteristics such as daily activity pattern.

• Journal of the Korean Dietetic Association
• /
• v.22 no.2
• /
• pp.131-142
• /
• 2016

The purpose of this study was to assess the physical activity and energy balance of weekdays and weekend in male high school students. Fifty healthy male high school students participated in this study. Anthropometric data were collected. Physical activity level (PAL) and energy intake for weekdays and weekend were calculated from a physical activity diary and food diary using the 24-hour recall method and interview. The resting metabolic rate (RMR) and estimated energy requirement (EER) were calculated from the prediction equations suggested in 2015 KDRIs. Total energy expenditure (TEE) was calculated by multiplying RMR by PAL. Mean age of subjects was $15.9{\pm}0.33years$. The daily pedometer counts were significantly higher in the weekdays (12,837 steps) than in weekend (6,661 steps) (P<0.001). The PAL of the weekdays ($1.63{\pm}0.17$) was significantly higher than that ($1.37{\pm}0.26$) of the weekend (P<0.001). PAL was significantly correlated with pedometer counts on the weekdays (r=0.495) and weekend (r=0.686). The total energy intakes ($2,847.2{\pm}681.5kcal$) and TEE ($3,046.3{\pm}437.3kcal$) of weekdays were significantly higher than those of the weekend. The results of this study would be useful to develop nutrition and exercise programs for male high school students on weekdays and weekend, respectively.

• Nutrition Research and Practice
• /
• v.6 no.1
• /
• pp.51-60
• /
• 2012

Weight-controlling can be supported by a proper prescription of energy intake. The individual energy requirement is usually determined through resting energy expenditure (REE) and physical activity. Because REE contributes to 60-70% of daily energy expenditure, the assessment of REE is very important. REE is often predicted using various equations, which are usually based on the body weight, height, age, gender, and so on. The aim of this study is to validate the published predictive equations for resting energy expenditure in 76 normal weight and 52 obese Korean children and adolescents in the 7-18 years old age group. The open-circuit indirect calorimetry using a ventilated hood system was used to measure REE. Sixteen REE predictive equations were included, which were based on weight and/or height of children and adolescents, or which were commonly used in clinical settings despite its use based on adults. The accuracy of the equations was evaluated on bias, RMSPE, and percentage of accurate prediction. The means of age and height were not significantly different among the groups. Weight and BMI were significantly higher in obese group (64.0 kg, $25.9kg/m^2$) than in the non-obese group (44.8 kg, $19.0kg/m^2$). For the obese group, the Molnar, Mifflin, Liu, and Harris-Benedict equations provided the accurate predictions of > 70% (87%, 79% 77%, and 73%, respectively). On the other hand, for non-obese group, only the Molnar equation had a high level of accuracy (bias of 0.6%, RMSPE of 90.4 kcal/d, and accurate prediction of 72%). The accurate prediction of the Schofield (W/WH), WHO (W/WH), and Henry (W/WH) equations was less than 60% for all groups. Our results showed that the Molnar equation appears to be the most accurate and precise for both the non-obese and the obese groups. This equation might be useful for clinical professionals when calculating energy needs in Korean children and adolescents.

• Journal of Korean Academy of Nursing
• /
• v.50 no.2
• /
• pp.191-199
• /
• 2020

Purpose: The purpose of this study was to compare sociodemographic characteristics of a normal cognitive group and mild cognitive impairment group, and establish prediction models of Mild Cognitive Impairment (MCI). Methods: This study was a secondary data analysis research using data from "the 4th Korea Longitudinal Study of Ageing" of the Korea Employment Information Service. A total of 6,405 individuals, including 1,329 individuals with MCI and 5,076 individuals with normal cognitive abilities, were part of the study. Based on the panel survey items, the research used 28 variables. The methods of analysis included a χ²-test, logistic regression analysis, decision tree analysis, predicted error rate, and an ROC curve calculated using SPSS 23.0 and SAS 13.2. Results: In the MCI group, the mean age was 71.4 and 65.8% of the participants was women. There were statistically significant differences in gender, age, and education in both groups. Predictors of MCI determined by using a logistic regression analysis were gender, age, education, instrumental activity of daily living (IADL), perceived health status, participation group, cultural activities, and life satisfaction. Decision tree analysis of predictors of MCI identified education, age, life satisfaction, and IADL as predictors. Conclusion: The accuracy of logistic regression model for MCI is slightly higher than that of decision tree model. The implementation of the prediction model for MCI established in this study may be utilized to identify middle-aged and elderly people with risks of MCI. Therefore, this study may contribute to the prevention and reduction of dementia.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.11 no.2
• /
• pp.91-98
• /
• 2017

Background & Object: Basic daily activity screening tool such as the Modified Barthel Index (MBI) has been used commonly in rehabilitation clinic and community based rehabilitation setting. Previous studies have shown the significant relations between the level of daily activities and driving ability on stroke or elderly people. However, there is a lack of studies to investigate the usefulness of MBI on prediction of driving ability for stroke patient. This study was to predict driving abilities of stroke survivor using Korean version Modified Barthel Index (K-MBI). Methods: A sample of 48 patients with stroke in rehabilitation hospital was recruited. All participants were tested level of basic daily activities using K-MBI. The driving ability of participants was tested using virtual reality driving simulator. The predictive validity was calculated of the K-MBI among pass or fail group of driving simulator test using receiver operating characteristics curves. Results: The cut-off score of >86.5 on the K-MBI is proper sensitivity to predict on driving performance ability. Conclusion: This pilot result offers clinical reference to therapists and caregivers for reasoning on driving recommendation period during rehabilitation stage of stroke survivors. Further studies need to identify prediction using real on-road test in a large population group.

• Korean Journal of Human Ecology
• /
• v.24 no.6
• /
• pp.811-827
• /
• 2015

The purpose of this study was to explore the factors related to depression of female elderly living alone by region. Data for this study was based on the 2011 National Survey on Elderly. Total of 1,684(689 rural elderly, 995 urban) community samples of female elderly living alone participated in this study. Even though there was no difference of depression score by region, this study found that the effects of factors on depression were significantly different by region. Yearly income, subjective health, balanced exchange of emotional support and satisfaction with their children were significantly associated with depression of both rural and urban female elderly living alone. For rural female elderly living alone, average daily television viewing time, number of close friends and frequency of contact with friends/neighbors were significant predictors to their depression. In the case of urban female elderly living alone, exercise, frequency of message, email or telephone contact with friends/ neighbors and balanced exchange of economic support contributed significantly to the prediction of depression. Subjective health had the strongest effect on depression for both rural and urban female elderly living alone.

• Journal of KIBIM
• /
• v.9 no.2
• /
• pp.1-10
• /
• 2019

In this research, schedule optimization is defined as balancing the number of workers while keeping the demand and needs of the project resources, creating the perfect schedule for each activity. Therefore, when one optimizes a schedule, multiple potentials of schedule changes are assessed to get an instant view of changes that avoid any over and under staffing while maximizing productivity levels for the available labor cost. Optimizing the number of workers in the scheduling process is not a simple task since it usually involves many different factors to be considered such as the development of quantity take-offs, cost estimating, scheduling, direct/indirect costs, and borrowing costs in cash flow while each factor affecting the others simultaneously. That is why the optimization process usually requires complex computational simulations/modeling. This research attempts to find an optimal selection of daily maximum workers in a project while considering the impacts of other factors at the same time through OPEN BIM based multiple computer simulations in resource leveling. This paper integrates several different processes such as quantity take-offs, cost estimating, and scheduling processes through computer aided simulations and prediction in generating/comparing different outcomes of each process. To achieve interoperability among different simulation processes, this research utilized data exchanges supported by building SMART-IFC effort in automating the data extraction and retrieval. Numerous computer simulations were run, which included necessary aspects of construction scheduling, to produce sufficient alternatives for a given project.

• Journal of Astronomy and Space Sciences
• /
• v.23 no.3
• /
• pp.227-236
• /
• 2006

The fractal dimension is a quantitative parameter describing the characteristics of irregular time series. In this study, we use this parameter to analyze the irregular aspects of solar activity and to predict the maximum sunspot number in the following solar cycle by examining time series of the sunspot number. For this, we considered the daily sunspot number since 1850 from SIDC (Solar Influences Data analysis Center) and then estimated cycle variation of the fractal dimension by using Higuchi's method. We examined the relationship between this fractal dimension and the maximum monthly sunspot number in each solar cycle. As a result, we found that there is a strong inverse relationship between the fractal dimension and the maximum monthly sunspot number. By using this relation we predicted the maximum sunspot number in the solar cycle from the fractal dimension of the sunspot numbers during the solar activity increasing phase. The successful prediction is proven by a good correlation (r=0.89) between the observed and predicted maximum sunspot numbers in the solar cycles.

• Korean Journal of Social Welfare
• /
• v.68 no.3
• /
• pp.105-125
• /
• 2016

School absenteeism is considered one of the early predictors of school drop-out and serious delinquency or criminal behavior. The primary goal of the current study was to explore the protective and risk factors related to changing school absenteeism over time based on the ecological-systemic perspective. The data was derived from the Korean Children and Youth Panel Survey (KCYPS) using the 2011 and 2012 survey waves collected from 2,378 elementary school students. Using this data, Panel Fixed Effects Analysis was conducted. Major findings indicated that daily computer usage, parental abuse, school activity attendance, and school grades had an effect on students missing school days over time. Specifically, high levels of computer usage and parental abuse were related to increased school absenteeism, while high levels of school activity attendance and school grades were associated with decreased school absenteeism. These findings emphasized the importance of predictive intervention for children and suggested the need to construct a school absenteeism monitoring system in South Korea.