• 제목/요약/키워드: Personal Exercise System

검색결과 53건 처리시간 0.02초

도시.농촌 지역 초등학생의 가족환경, 건강행위 및 건강상태에 관한 비교 (Comparision of Family Environment, Health Behavior and Health State of Elementary Students in Urban and Rural Areas)

  • 배연숙;박경민
    • 지역사회간호학회지
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    • 제9권2호
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    • pp.502-517
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    • 1998
  • This research intends to survey family environment, health behavior and health status of the students in urban-rural elementary schools and analyze those factors comparatively, and use the result as basic material for school health teacher to teach health education in connection with family and regional areas. It also intends to improve a pupil's self-abilitiy in health care. The subjects involve 2,774 students of urban elementary schools and 583 student in rural ones, who were selected by means of a multi -stage probability sampling. Using the questionnaire and school documents, we collected data on family environment, health behavior and health status for 19 days. Feb. 2nd 1998 through Feb. 20th 1998. The R -form of Family Environment Scale (Moos, 1974) was used in the analysis of family environment(Cronbach's Alpha =0.80). Questionnaires of Health Behavior in School-aged children used by the WHO in Europe(Aaro et al., 1986) and the ones developed by the Health Promotion Committee of the Western Pacific(WHO, 1995)(adapted by long Young-suk and Moon Young-hee(1996)) were used in the analysis of health behavior, as well documents on absences due to sickness, school health room-visits, levels of physical strength, height, weight and degree of obesity were used to determine health status. In next step, We used them with an $X^2$-test, t-test, Odds Ratio, and a 95% Confidence Interval. 1. In two dimensions of three, family-relationship (t=3.41, p=0.001) and system -maintenances(t= 2.41, p=0.0l6) the mean score of urban children were significantly higher than those of rural ones. In the personal development dimension however, there was little significant difference. Assorting family environment into 10 sub-fields and analyzing them, we recognized that urban children were superior to rural children in the sub-fields of expressiveness (t =3.47, p=0.001), conflict (t=0.48, p=0.001), active-recreational orientation (t = 1.97, p=0.049) and organization (t=4.33, p=0.000). 2. Referring to the Odds Ratios of urban-rural children's health behaviors, urban children set up more desirable behavior than rural children wear ing safety belts (Odds Ratio =0.32, p=0.000), washing hands after meals(Odds Ratio = 0.43, p= 0.000), washing hands after excreting (Odds Ratio = 0.39, p=O.OOO), washing hands after coming - home ( Odds Ratio = 0.75, p = 0.003), brushing teeth before sleeping(Odds Ratio =0.45, p=0.000), brushing teeth more than once a day (Odds Ratio =0.73, p=0.0l2), drinking boiled water (Odds Ratio = 0.49, p=0.000), collecting garbage at home(Odds Ratio=0.31, p=0.000) and in the school(Odds Ratio =0. 67, p=0.000). All these led to significant differences. As to taking milk(Odds Ratio = 1.50, p=0.000), taking care of eyesight(Odds Ratio=1.41, p=0.001) and getting physical exercise in(Odds Ratio = 1.33, p=0.0l9) and outside the school(Odds Ratio = 1.32, p=0.005), rural children had more desirable behavior which also revealed a significant difference. There was little significant difference in smoking, but the smoking rate of rural children(5.5%) was larger than that of urban children(3.9%). 3. Health status was analyzed in terms of absences, school health room-visits, levels of physical strength, and the degree of obesity, height and weight. Considering Odds Ratios of the health status of urban-rural children, the health status of rural children was significantly better than that of the urban ones in the level of physical strength(t=1.51, p=0.000) and the degree of obesity(t=1.84, p=0.000). The mean height of urban children ($150.4{\pm}7.5cm$) is taller than that of their counterparts($149.5{\pm}7.9$), which revealed a significant difference (t =2.47, p=0.0l4). The mean weight of urban children($42.9{\pm}8.6kg$) is larger than that of their counterparts($41.8{\pm}9.0kg$), which was also a significant difference(t=2.81, p=0.005). Considering the results above, we can recognize that there are significant differences in family environment, health behavior, and health status in urban-rural children. These results also suggestion ideas for health education. What we would suggest for the health program of elementary schools is that school health teachers should play an active role in promoting the need and importance of health education, develop the appropriate programs which correspond to the regional characteristics, and incorporate them into schools to improve children's ability to manage their own health management.

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가정산소치료의 보험급여 실시 이후 처방 실태: 다기관 조사 -만성기도폐쇄성질환 임상연구센터 제3세부과제 만성기도폐쇄성질환 진료지침 개발/보급 연구- (Long-term Oxygen Therapy for Chronic Respiratory Insufficiency: the Situation in Korea after the Health Insurance Coverage: a Multi-center Korean Survey -Study for the Development and Dissemination of the COPD Guidelines, Clinical Research Center for Chronic Obstructive Airway Disease-)

  • 박명재;유지홍;최천웅;김영균;윤형규;강경호;이승룡;최혜숙;이관호;이진화;임성철;김유일;신동호;김태형;정기석;박용범
    • Tuberculosis and Respiratory Diseases
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    • 제67권2호
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    • pp.88-94
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    • 2009
  • Background: From November 2006, The national health insurance system in the Republic of Korea began to cover prescribed long-term oxygen therapy (LTOT) in patients with chronic respiratory insufficiency. This study examined the current status of LTOT after national health insurance coverage. Methods: Between November 1, 2006 and June 30, 2008, the medical records of patients who were prescribed LTOT by chest physicians were reviewed. The data was collected from 13 university hospitals. Results: 197 patients (131 male and 66 female) were prescribed LTOT. The mean age was 64.3${\pm}$13.0 years. The most common underlying disease was chronic obstructive pulmonary disease (n=103, 52.3%). Chest physicians prescribed LTOT using arterial blood gas analysis or a pulse oxymeter (74.6%), symptoms (14%), or a pulmonary function test (11.2%). The mean oxygen flow rate was 1.56${\pm}$0.68 L/min at rest, 2.08${\pm}$0.91 L/min during exercise or 1.51${\pm}$0.75 L/min during sleep. Most patients (98.3%) used oxygen concentrators. Only 19% of patients used ambulatory oxygen supplies. The oxygen saturation before and after LTOT was 83.18${\pm}$10.48% and 91.64${\pm}$7.1%, respectively. After LTOT, dyspnea improved in 81.2% of patients. The mean duration of LTOT was 16.85${\pm}$6.71 hours/day. The rental cost for the oxygen concentrator and related electricity charges were 48,414${\pm}$15,618 won/month and 40,352${\pm}$36,815 won/month, respectively. Approximately 75% of patients had a regular visit by the company. 5.8% of patients had personal pulse oxymetry. 54.9% of patients had their oxygen saturation checked on each visit hospital. 8% of patients were current smokers. The most common complaint with LTOT was the limitation of daily activity (53%). The most common complaint with oxygen concentrators was noise (41%). Conclusion: The patients showed good compliance with LTOT. However, only a few patients used an ambulatory oxygen device or had their oxygen saturation measured.

SVM을 이용한 VKOSPI 일 중 변화 예측과 실제 옵션 매매에의 적용 (VKOSPI Forecasting and Option Trading Application Using SVM)

  • 라윤선;최흥식;김선웅
    • 지능정보연구
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    • 제22권4호
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    • pp.177-192
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    • 2016
  • 기계학습(Machine Learning)은 인공 지능의 한 분야로, 데이터를 이용하여 기계를 학습시켜 기계 스스로가 데이터 분석 및 예측을 하게 만드는 것과 관련한 컴퓨터 과학의 한 영역을 일컫는다. 그중에서 SVM(Support Vector Machines)은 주로 분류와 회귀 분석을 목적으로 사용되는 모델이다. 어느 두 집단에 속한 데이터들에 대한 정보를 얻었을 때, SVM 모델은 주어진 데이터 집합을 바탕으로 하여 새로운 데이터가 어느 집단에 속할지를 판단해준다. 최근 들어서 많은 금융전문가는 기계학습과 막대한 데이터가 존재하는 금융 분야와의 접목 가능성을 보며 기계학습에 집중하고 있다. 그러면서 각 금융사는 고도화된 알고리즘과 빅데이터를 통해 여러 금융업무 수행이 가능한 로봇(Robot)과 투자전문가(Advisor)의 합성어인 로보어드바이저(Robo-Advisor) 서비스를 발 빠르게 제공하기 시작했다. 따라서 현재의 금융 동향을 고려하여 본 연구에서는 기계학습 방법의 하나인 SVM을 활용하여 매매성과를 올리는 방법에 대해 제안하고자 한다. SVM을 통한 예측대상은 한국형 변동성지수인 VKOSPI이다. VKOSPI는 금융파생상품의 한 종류인 옵션의 가격에 영향을 미친다. VKOSPI는 흔히 말하는 변동성과 같고 VKOSPI 값은 옵션의 종류와 관계없이 옵션 가격과 정비례하는 특성이 있다. 그러므로 VKOSPI의 정확한 예측은 옵션 매매에서의 수익을 낼 수 있는 중요한 요소 중 하나이다. 지금까지 기계학습을 기반으로 한 VKOSPI의 예측을 다룬 연구는 없었다. 본 연구에서는 SVM을 통해 일 중의 VKOSPI를 예측하였고, 예측 내용을 바탕으로 옵션 매매에 대한 적용 가능 여부를 실험하였으며 실제로 향상된 매매 성과가 나타남을 증명하였다.