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A Study on the Prediction of Fall Factors for the Elderly Living in the City

도시 생활 노인의 낙상요인 예측에 관한 연구

  • 이현주 (건양대학교 물리치료학과) ;
  • 이태용 (충남대학교 의학전문대학원 예방의학과) ;
  • 태기식 (건양대학교 의공학부)
  • Received : 2018.02.03
  • Accepted : 2018.02.07
  • Published : 2018.02.28

Abstract

The purpose of this study was to investigate the factors affecting falls in 107 elderly living in the city aged 65 or older by evaluating general characteristics, chronic disease status, medical variables related to falls, balance-related confidence, physical ability and depression. Also, the correlations between the significant differences in variables were identified, and the prediction power was determined by deriving the variables with high influence to induce the fall. In the faller group, urinary incontinence, foot pain, lower extremity weakness, number of chronic disease and medication use were significantly higher than those of the nonfaller group. Also, statistically significant differences were evaluated in ABC (Activities-specific Balance Confidence) score, BBS (Berg Balance Scale) score, SGDS (Short Geriatric Depression Scale), FRT (Functional Reach Test) value. The main correlated factor for fall was ABC score, the lower the ABC score, fall risk is increased which is a significant negative impact. When the evaluation is performed by combining those scales, the hit ratio to classify whether faller or nonfaller is increased to 70.01% which is quite higher value.

본 연구는 65세 이상 도시거주 노인 107명을 대상으로 일반적 특성, 만성질환 상태, 낙상 관련 의학적 변수, 균형 관련 자신감, 신체적 능력, 우울감을 평가하는 도구를 통해 낙상에 영향을 미치는 관련 주 요인을 찾고자 하였다. 또한 유의한 차이가 있는 변수들 간의 상관관계를 파악하며, 이 중 낙상을 유발하는 데 높은 영향력이 있는 변수를 도출하여 예측력을 알아보았다. 연구 결과, 낙상군에서 요실금, 발의 통증, 하지근력약화, 만성 질환수 및 복용 약물수 빈도수가 비낙상군에 비해 통계적으로 유의하게 높았다. 또한 ABC (Activities-specific Balance Confidence) 총점, BBS (Berg Balance Scale) 총점, SGDS (Short Geriatric Depression Scale) 총점, FRT(Functional Reach Test) 값에서 통계학적으로 유의한 차이가 있었다. 낙상에 영향을 주는 주요인은 ABC 총점으로 점수가 낮을수록 낙상 위험이 높아짐으로써 균형능력에 대한 자기 확신감이 낮을수록 낙상의 가능성이 높아지는 것으로 나타났으며, ABC, SDGS, BBS 척도가 결합하여 적용될 경우 낙상군과 비낙상군을 구분하는 예측력은 70.1%로 높게 나타났다.

Keywords

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