Clinical factors as predictors of the risk of falls and subsequent bone fractures due to osteoporosis in postmenopausal women

J Bone Miner Metab. 2006;24(5):419-24. doi: 10.1007/s00774-006-0704-1.

Abstract

In Japan, the "bedridden state" is one of the most serious problems the aged face, and it is becoming a social problem. The main causes of the bedridden state are cerebrovascular disorders and bone fractures following falls. The purpose of this study was to predict risk factors for falls and resultant bone fracture due to osteoporosis. We explored mobility parameters for possible fall prevention. In order to examine the correlation between the risk of falling and resultant bone fracture due to osteoporosis, logistic regression analysis was performed between bone mass (independent variable) and various factors dependent variables: body mass index [BMI], body fat percentage, atherogenic index, presence of transformation-related osteoarthritis of knee, presence of transformation-related osteoarthritis of spine, maximum step length, single-leg stance with open eyes, and hip-joint flexion motion angle); predictive factors were then examined. Predictive factors were determined by the stepwise method. Subjects who could not perform the "single-leg stance with open eyes" test had a risk of falling and bone fracture 2.49 times as large as that of subjects who could. The "single-leg stance with open eyes" test may be considered a useful method for the early detection of the risk of falling and bone fracture associated with osteoporosis. As a first step to identify factors predicting the occurrence of falls and bone fractures due to osteoporosis, we intended to discover an indicator that would help to detect incipient osteoporosis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Accidental Falls* / statistics & numerical data
  • Aged
  • Bone Density / physiology
  • Female
  • Fractures, Bone / epidemiology*
  • Humans
  • Logistic Models
  • Osteoporosis, Postmenopausal / physiopathology*
  • Predictive Value of Tests
  • Risk Factors