• Sleep Medicine and Psychophysiology
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• v.19 no.2
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• pp.68-76
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• 2012

Objectives: Sleep disorders cause changes of autonomic nervous system (ANS) which affect cardiovascular system. Primary insomnia (PI) makes acceleration of sympathetic nervous system (SNS) tone by sleep deficiency and arousal. Obstructive sleep apnea syndrome (OSAS) sets off SNS by frequent arousals and hypoxemias during sleep. We aimed to compare the changes of heart rate variability (HRV) indices induced by insomnia or sleep apnea to analyze for ANS how much to be affected by PI or OSAS. Methods: Total 315 subjects carried out nocturnal polysomnography (NPSG) were categorized into 4 groups - PI, mild, moderate and severe OSAS. Severity of OSAS was determined by apnea-hypopnea index (AHI). Then we selected 110 subjects considering age, sex and valance of each group's size [Group 1 : PI (mean age=$41.50{\pm}13.16$ yrs, AHI <5, n=20), Group 2 : mild OSAS (mean age=$43.67{\pm}12.11$ yrs, AHI 5-15, n=30), Group 3 : moderate OSAS (mean age $44.93{\pm}12.38$ yrs, AHI 16-30, n=30), Group 4 : severe OSAS (mean age=$45.87{\pm}12.44$ yrs, AHI >30, n=30)]. Comparison of HRV indices among the four groups was performed with ANCOVA (adjusted for age and body mass index) and Sidak post-hoc test. Results: We found statistically significant differences in HRV indices between severe OSAS group and the other groups (PI, mild OSAS and moderate OSAS). And there were no significant differences in HRV indices among PI, mild and moderate OSAS group. In HRV indices of PI and severe OSAS group showing the most prominent difference in the group comparisons, average RR interval were $991.1{\pm}27.1$ and $875.8{\pm}22.0$ ms (p=0.016), standard deviation of NN interval (SDNN) was $85.4{\pm}6.6$ and $112.8{\pm}5.4$ ms (p=0.022), SDNN index was $57.5{\pm}5.2$ and $87.6{\pm}4.2$ (p<0.001), total power was $11,893.5{\pm}1,359.9$ and $18,097.0{\pm}1,107.2ms^2$(p=0.008), very low frequency (VLF) was $7,534.8{\pm}1,120.1$ and $11,883.8{\pm}912.0ms^2$ (p=0.035), low frequency (LF) was $2,724.2{\pm}327.8$ and $4,351.6{\pm}266.9ms^2$(p=0.003). Conclusions: VLF and LF which were correlated with SNS tone showed more increased differences between severe OSAS group and PI group than other group comparisons. We could suggest that severe OSAS group was more influential to increased SNS activity than PI group.

• Tuberculosis and Respiratory Diseases
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• v.45 no.4
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• pp.723-735
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• 1998

Background : Vitamin C has been reported to have a role in the decrease of airway hyperresponsiveness in animal models. This data is based on some metabolic actions of vitamin C, such as promotion of histamine degradation, producing more $PGE_2$ than $PGF_{2\alpha}$ in cyclooxygenase pathway, decrease of smooth muscle contraction, and acting as reducing agent of oxidant. It has been also known that heavy smokers have lower blood levels of vitamin C than nonsmokers and this deficiency in heavy smokers have been explained by several mechanisms, such as increased oxidation by oxidants and free radicals, increased biosynthesis of catecholamine and serotonin released by nicotine, and inadequate dietary intake. In this study, We attempted to assess effect of vitamin C on bronchial hyperresponsiveness in heavy smokers who have bronchial hyperresponsiveness and role of vitamin C on bronchial hyperresponsiveness. Method: To assess acute effect of vitamin C on airway hyperresponsiveness, blood sample for vitamin C level and spirometry, methacholine challenge test were done in 17 smokers and 8 nonsmokers, and one hour after oral administration of vitamin C 3 g, blood sample for vitamin C level and spirometry, methacholine challenge test were repeated. To assess chronic effect of vitamin C on airway hyperresponsiveness, after daily administration of vitamin C 1 g for one week in 17 smokers, blood sample for vitamin C level and spirometry, methacholine challenge test were done. To assess role of vitamin C, after oral administration of vitamin C 3 g plus indomethacin 100 mg in 12 of 15 smokers who were reactive to methacholine challenge test, spirometry and methacholine challenge test were done and after oral intake of indomethacin 100 mg in 12 smokers who were reactive to methacholine challenge test, spirometry and methacholine challenge test were repeated. Result: There were no significant differences in whole blood vitamin C levels between smokers($1.17{\pm}0.22$ mg/dL) and nonsmcikers($1.14{\pm}0.19$ mg/dL) (p>0.05). Fifteen of the 17 smokers(88.2%) were reactive to methacholine challenge test and 10 of the 15 smokers who were reactive to methacholine challenge test were less than 8 mg/dL in $PC_{20}FEV-2$, and 7 of the 8 nonsmokers(87.5%) were nonreactive to methacholine challenge test There were significant decrease in bronchial responsiveness after oral administration of vitamin C 3 g in 13 of the 15 smokers who were reactive to methacholine challenge test This significant decrease persisted with maintenance daily administration of 1 g for one week. $PC_{20}FEV-2$ were not correlated to vitamin C levels in smokers. After oral administration of indomethacin 100 mg, significant reduction of bronchial responsiveness that occured after oral administration of vitamin C 3 g in smokers were attenuated. Conclusion: Although there were no significant differences in whole blood vitamin C levels between smokers and nonsmokers. heavy smokers have significant increase in bronchial responsiveness than nonsmokers. This bronchial hyperresponsiveness of heavy smokers can be attenuated by vitamin C supplement. Disappearance of vitamin C effect by indomethacin supplement may suggest that vitamin C exert its effect via alteration of arachidonic acid metabolism.

• Tuberculosis and Respiratory Diseases
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• v.47 no.6
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• pp.824-835
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• 1999

Background: Sleep-related breathing disorders are commonly found in patients with chronic renal failure and particularly, sleep apnea may have an influence on the long-term mortality rates in these patients. Maintenance hemodialysis is the mainstay of medical measures for correcting the metabolic derangements of chronic renal failure but it is uncertain whether it may alleviate sleep disorders including sleep apnea. Methods: Forty seven patients on maintenance hemodialysis were surveyed with the sleep questionnaire about their clinical symptoms related to sleep disorders. Among them, 15 patients underwent the polysomnography and their blood levels of urea nitrogen, creatinine, electrolytes and the arterial blood gases in the nights before and following hemodialysis were measured. Results: Forty(85.1%) of the 47 patients complained of the symptoms associated with sleep-wake cycle disturbances, 55.3% experienced snoring and 27.7% reported witnessed apneas. The duration of REM sleep increased significantly in the nights after hemodialysis compared to the nights without hemodialysis(p<0.05) and the percentage of total sleep time comprising NREM sleep decreased significantly in the nights following hemodialysis compared to the nights before hemodialysis(p<0.05). The percentage of total sleep time consisting of the stage 1 and 2 NREM sleep showed the trend for a decrease in the nights after hemodialysis(p=0.051), while the percentage of total sleep time comprising the stage 3 and 4 NREM sleep did not change between nights. The obstructive sleep apnea was more predominant type than the central one in both nights and there were no differences in the apnea index and the apnea-hypopnea index between the nights. The decrease in the blood level of urea nitrogen, creatinine, potassium and phosphorus was observed after hemodialysis(p<0.05), but the differences of parameters measured during polysomnography between the nights did not correlate with the changes of biochemical factors obtained on the two nights. Arterial blood gas analysis showed that pH was significantly greater in the nights after hemodialysis than in the nights before hemodialysis(p<0.05), but there were no correlations between the parameters examined during polysomnography and the parameters of arterial blood gas analysis(p<0.05). Conclusion: These results suggest that chronic renal failure is an important systemic disorder which is strongly associated with sleep disorders. Maintenance hemodialysis, although it is a widely accepted measure to treat chronic renal failure, did not significantly modulate the sleep architecture and the severity of sleep apnea. Thus, taking the patients with chronic renal failure into account, it is advisable to try not only to find a substantial way for correcting metabolic derangements but also to consider the institution of more effective treatments for sleep disorders.

• Tuberculosis and Respiratory Diseases
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• v.47 no.4
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• pp.442-450
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• 1999

Background : Isoniazid(INH) and rifampicin(RFP) are the most effective anti-tuberculosis drugs which make the short-course chemotherapy possible. Although prescribed dosages of INH and RFP in Korea are different from those recommended by American Thoracic Society, there has been few study about pharmacokinetic profiles of INH and RFP in Korean patients who receive INH, RFP, ethambutol(EMB) and pyrazinamide(PZA) simultaneously. Methods : Among the patients with active tuberculosis from Dec. 1997 to July 1998, we selected 17 patients. After an overnight fast, patients were given INH 300mg, RFP 450mg, EMB 800mg and PZA 1500mg daily. Blood samples for the measurement of plasma INH(n=15) and RFP(n=17) level were drawn each at 0, 0.5, 1, 1.5, 2, 4, 6, 8 and 12hrs, and urine was also collected. INH and RFP level in the plasma and the urine were measured by high-performance liquid chromatography(HPLC). Pharmacokinetic parameters such as peak serum concentration(Cmax), time to reach to peak serum concentration(Tmax), half-life, elimination rate constant(Ke), total body clearance(CLtot), nonrenal clearance(CLnr), and renal clearance(CLr) were calculated. Results : 1) Pharmacokinetic parameters of INH were as follows: Cmax; $7.63{\pm}3.20{\mu}g/ml$, Tmax; $0.73{\pm}0.22hr$, half-life; $2.12{\pm}0.84hrs$, Ke; $0.83{\pm}0.15hrs^{-1}$, CLtot; $17.54{\pm}8.89L/hr$, CLnr; $14.74{\pm}8.35L/hr$, CLr; $2.79{\pm}1.31L/hr$. 2) Pharmacokinetic parameters of RFP were as follows: Cmax; $8.93{\pm}3.98{\mu}g/ml$, Tmax; $1.76{\pm}1.13hrs$, half-life; $2.27{\pm}0.54hrs$, Ke; $0.32{\pm}0.08hrs^{-1}$, CLtot; $14.63{\pm}6.60L/hr$, CLr; $1.04{\pm}0.55L/hr$, CLnr; $13.59{\pm}6.21L/hr$. 3) While the correlation between body weight and Cmax of INH was not statistically significant (r=-0.514, p value>0.05), Cmax of RFP was significantly affected by body weight of the patients(r=-0.662, p value<0.01). Conclusion : In Korean patients with tuberculosis, 300mg of INH will be sufficient to reach the ideal peak blood level even in the patients over 50kg of body weight However, 450mg of RFP will not be the adequate dose in the patients who weigh over 50~60kg.

• Tuberculosis and Respiratory Diseases
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• v.51 no.6
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• pp.559-569
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• 2001

Background : Pleural effusion is one of the most common clinical manifestations associated with a variety of pulmonary diseases such as malignancy, tuberculosis, and pneumonia. However, there are no useful laboratory tests to determine the specific cause of pleural effusion. Therefore, an attempt was made to analyze the various types of pleural effusion and search for useful laboratory tests for pleural effusion in order to differentiate between the diseases, especially between a malignant pleural effusion and a non-malignant pleural effusion. Methods : 93 patients with a pleural effusion, who visited the Severance hospital from January 1998 to August 1999, were enrolled in this study. Ultrasound-guided thoracentesis was done and a confirmational diagnosis was made by a gram stain, bacterial culture, Ziehl-Neelsen stain, a mycobacterial culture, a pleural biopsy and cytology. Results : The male to female ratio was 56 : 37 and the average age was $47.1{\pm}21.8$ years. There were 16 cases with a malignant effusion, 12 cases with a para-malignant effusion, 36 cases with tuberculosis, 22 cases with a para-pneumonic effusion, and 7 cases with transudate. The LDH2 fraction was significantly higher in the para-malignant effusion group compared to the para-pneumonic effusion group [$30.6{\pm}6.4%$ and $20.2{\pm}7.5%$, respectively (p<0.05)] and both the LDH1 and LDH2 fraction was significantly in the para-malignant effusion group compared to those with tuberculosis [$16.4{\pm}7.2%$ vs. $7.6{\pm}4.7%$, and $30.6{\pm}6.4%$ vs.$17.6{\pm}6.3%$, respectively (p<0.05)]. The pleural effusion/serum LDH4 fraction ratio was significantly lower in the malignant effusion group compared to those with tuberculosis [$1.5{\pm}0.8$ vs. $2.1{\pm}0.6$, respectively (p<0.05)]. The LDH4 fraction and the pleural effusion/serum LDH4 fraction ratio was significantly lower in the para-malignant effusion group compared to those with tuberculosis [$17.0{\pm}5.8%$ vs. $23.5{\pm}4.6%$ and $1.3{\pm}0.4$ vs. $2.1{\pm}0.6$, respectively (p<0.05)]. Conclusion : These results suggest that the LDH isoenzyme was the only useful biochemical test for a differential diagnosis of the various diseases. In particular, the most useful test was the pleural effusion/serum LDH4 fraction ratio to distinguish between a para-malignant effusion and a tuberculous effusion.

• Tuberculosis and Respiratory Diseases
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• v.45 no.2
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• pp.380-387
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• 1998

Background: Minimal pressure support(PSmin) is a level of pressure support which offset the imposed work of breathing(WOBimp) developed by endotracheal tube and ventilator circuits in pressure support ventilation While the lower applied level of pressure support compared to PSmin could induce respiratory muscle fatigue, the higher level than PSmin could keep respiratory muscle rest resulting in prolongation of weaning period during weaning from mechanical ventilation PSmin has been usually applied in the level of 5~10 cm$H_2O$, but the accurate level of PSmin is difficult to be determinated in individual cases. PSmin is known to be calculated by using the equation of "PSmin = peak inspiratory flow rate during spontaneus ventilation$\times$total ventilatory system resistance", but correlation of calculated PSmin and measured PSmin has not been known. The objects of this study were firstly to assess whether customarily applied pressure support level of 5~10 cm$H_2O$ would be appropriate to offset the imposed work of breathing among the patients under weaning process, and secondly to estimate the correlation between the measured PSmin and calculated PSmin. Method : 1) Measurement of PSmin : Intratracheal pressure changes were measured through Hi-Lo jet tracheal tube (8mm in diameter, Mallinckroft, USA) by using pulmonary monitor(CP-100 pulmonary monitor, Bicore, USA), and then pressure support level of mechanical ventilator were increased until WOBimp was reached to 0.01 J/L or less. Measured PSmin was defined as the lowest pressure to make WOBimp 0.01 J/L or less. 2) Calculation of PSmin : Peak airway pressure(Ppeak), plateau airway pressure(Pplat) and mean inspiratory flow rate of the subjects were measured on volume control mode of mechanical ventilation after sedation. Spontaneous peak inspiratory flow rates were measured on CPAP mode(O cm$H_2O$). Thereafter PSmin was calculated by using the equation "PSmin = peak inspiratory flow rate$\times$R, R = (Ppeak-Pplat)/mean inspiratory flow rate during volume control mode on mechanical ventilation". Results: Sixteen patients who were considered as the candidate for weaning from mechanical ventilation were included in the study. Mean age was 64(${\pm}14$) years, and the mean of total ventilation times was 9(${\pm}4$) days. All patients except one were males. The measured PSmin of the subjects ranged 4.0~12.5cm$H_2O$ in 14 patients. The mean level of PSmin was 7.6(${\pm}2.5\;cmH_2O$) in measured PSmin, 8.6 (${\pm}3.25\;cmH_2O$) in calculated PSmin Correlation between the measured PSmin and the calculated PSmin is significantly high(n=9, r=0.88, p=0.002). The calculated PSmin show a tendancy to be higher than the corresponding measured PSmin in 8 out of 9 subjects(p=0.09). The ratio of measured PSmin/calculated PSmin was 0.81(${\pm}0.05$). Conclusion: Minimal pressure support levels were different in individual cases in the range from 4 to 12.5 cm$H_2O$. Because the equation-driven calculated PSmin showed a good correlation with measured PSmin, the application of equation-driven PSmin would be then appropriate compared with conventional application of 5~10 cm$H_2O$ in patients under difficult weaning process with pressure support ventilation.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.

• Journal of Preventive Medicine and Public Health
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• v.35 no.2
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• pp.141-146
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• 2002

Objectives : This study was peformed in order to assess the incidence of hypertension based on two-years follow-up of a rural hypertension-free cohort in Korea. Methods : The study cohen comprised 2,580 subjects aged above 20 (1,107 men and 1,473 women) of Chung-Song County in Kyungpook Province judged to be hypertensive-free at the baseline examination in 1996. For each of two examinations in the two-year follow-up, those subjects free of hypertension were followed for the development of hypertension to the next examination one year (1997) and two years later (1998). The drop-out rate was 24.7% in men and 19.6% in women. Hypertension was defined as follows 1) above mild hypertension as a SBP above 140 mmHg or a DBP above 90 mmMg,2) above moderate hypertension as a SBP above 160 mmHg or a DBP above 100 mmHg or when the participant reported having used antihypertensive medication after beginning this survey. Results : The age-standardized incidence of above mild hypertension was 6 per 100 person years (PYS) in men and that of above moderate hypertension was 1.2. In women, the age-standardized rate for above mild hypertension was 5.7 and 1.5 for above mild and moderate hypertension, respectively. However, the rates of incidence as calculated by the risk method were 4.8% and 1.0% in men and 4.6%, 1.2% in women, respectively. In both genders, incidence was significantly associated with advancing age(p<0.01), In men, the incidences of above moderate hypertension by age group were 0.5 per 100 PYS aged 20-39, 0.7 aged 40-49, 1.7 aged 50-59, 3.6 aged 60-69, and 5.8 aged above 70(p<0.01). In women, those the incidence measured 0.6 per 100 PYS aged 20-39, 1.8 aged 40-49, 1.3 aged 50-59, 3.3 aged 60-69, and 5.6 aged above 70(p<0.01). After age 60, the incidence of hypertension increased rapidly. Conclusions : The incidence data of hypertension reported in this study may serve as a reference data for evaluating the impact of future public efforts in the primary prevention of hypertension in Korea.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.4
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• pp.101-118
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• 2023

During the COVID-19 pandemic, the utilization rate of neighborhood parks and green spaces increased significantly, and the outbreak served as an opportunity to highlight the values and functions of neighborhood parks and green spaces for urban residents. This study aims to empirically analyze how citizens' movement and the use of neighborhood parks and green spaces changed before and after COVID-19 and examine the social and spatial characteristics that affected these changes. As a research method, first, people's mobility around neighborhood parks and green spaces before and after the COVID-19 pandemic were compared using signal data from telecommunication carriers. Through the analysis of changes in residence time and movement volume, the movement characteristics of citizens after COVID-19 and changes in walking-based park visits were examined. Second, the factors affecting the mobility change in neighborhood parks and green spaces were analyzed. The social and spatial characteristics that affect citizens' visits to neighborhood parks and green spaces before and after COVID-19 were examined through correlation and multiple regression analysis. Subsequently, through cluster analysis, the types of living areas for the post-COVID era were classified from the perspective of the supply and management of neighborhood parks and green spaces services, and directions for improving neighborhood parks and green spaces by type were presented. Major research findings are as follows: First, since the outbreak of COVID-19, activities within 500m of the residence have increased. The amount of stay and walking movement increased in both 2020 and 2021, which means that the need to review the quantitative standards and attractions of neighborhood parks and green spaces has increased considering the changed scope of the walking and living area. Second, the overall number of visits to neighborhood parks and green spaces by walking has increased since the outbreak of COVID-19. The number of visits to neighborhood parks and green spaces centered on the house and the workplace increased significantly. The park green policy in the post-COVID era should be promoted by discovering underprivileged areas, focusing on areas where residential, commercial, and business facilities are concentrated, and improving neighborhood parks and green services in quantitative and qualitative terms. Third, it was found that the higher the level of park green service, the higher the amount of walking movement. It is necessary to use indicators that contribute to improving citizens' actual park green services, such as walking accessibility, rather than looking at the criteria for securing green areas. Fourth, as a result of cluster analysis, five types of neighborhood parks and green spaces were derived in response to the post-COVID era. This suggests that it is necessary to consider the socioeconomic status and characteristics of living areas and the level of park green services required in future park green policies. This study has academic and policy significance in that it has laid the basis for establishing neighborhood parks and green spaces policy in response to the post-COVID era by using various analysis methodologies such as carrier signal data analysis, GIS analysis, and statistical analysis.