Introduction 2001 2003a 2001 2006 2000 2003 2001 2003 2003 2004 2005 2006 2002 1997 2005 1999 2005c 2005 2005 2007 2005c 2002 If low muscle strength indeed is a physiological cause of falls, this raises the question whether people with a high risk of falling can be identified by means of relatively simple maximum muscle strength measures. The aim of this study was to find the best predictor of falls after a gait perturbation in a standardized situation, from a range of muscle strength measures in older adults. 2003 2005b 2004 1990 2002 1990 1999 2004 2006 Elderly volunteers performed these maximum strength capacity tests and we measured their ability to prevent a fall after being tripped. We hypothesized that maximum muscle strength measures can be used to differentiate fallers from non-fallers. If so, this would not only allow identification of potential fallers, but also indicate specific limiting factors to target with exercise-based interventions. Methods Seventeen healthy older adults participated [10 women: age 71 (SD 4.5) years, mass 75 (SD 9) kg, height 1.68 (SD 0.09) m]. All participants were fit and had no orthopedic, neuromuscular, cardiac or visual problems. The Ethics Committees the VU Medical Center and of the Manchester Metropolitan University approved the procedure and all subjects gave their written, informed consent before participation. Capacity measures 2004 2004 1 2005a ankle max knee max ankle rtd knee rtd Fig. 1 a b c d e  1 2005a c legpress max legpress rfd 1 jump h hand f Tripping measurements and falls 2005c 1 1994 2000 Statistical analysis 1994 p  Results Tripping measurements and falls p Capacity measurement as predictor for falls legpress max jump h hand f 1 p  legpress max p  legpress rfd p  legpress max p  legpress rfd r p  legpress max r p  legpress rfd Table 1 Correlation coefficients between capacity measures  ankle rtd knee max knee rtd legpress max legpress rfd jump h hand f ankle max 0.63 b 0.23 0.32 0.76 b 0.37 0.61 b 0.40 ankle rtd 1 0.36 0.70 b 0.68 a 0.40 0.65 b 0.51 a knee max  1 0.72 b 0.53 a −0.02 0.34 0.71 b knee rtd   1 0.57 a 0.36 0.55 a 0.78 b legpress max    1 0.33 0.82 b 0.59 a legpress rfd     1 0.29 0.23 jump h      1 0.69 b  bold a p  b p  2 Fig. 2 ankle max ankle rtd knee max knee rtd legpress max legpress rfd jump h hand f p  p  legpress max 2 2 3 jump h hand f legpress max Table 2 Predictive variables resulting from stepwise discriminant analyses and cross-validation on capacity measures Capacity measures Predictive variable p Discriminant analysis (sensitivity/specificity%) Cross-validation (sensitivity/specificity%) legpress max ankle max ankle max knee max knee rtd jump h hand f legpress max 0.001 86/100 a ankle max ankle rtd knee max knee rtd ankle max  knee max 0.007 71/90 71/90 jump h jump h 0.002 86/90 86/80 hand f hand f 0.003 86/80 86/80 a Fig. 3 legpress max jump h hand f legpress max jump h hand f  Discussion It is important to identify individuals most at risk of falling, because they should be considered with priority for receiving targeted exercise interventions aimed at reducing the incidence of falls. This study investigated the possibility to identify fallers from maximum strength measures that could be applied in clinical settings. The results showed that participants who fell after a gait perturbation could indeed be identified based on these measures. Especially, a classification model based on maximum leg press push-off force yielded high sensitivity and specificity in cross-validation. 2002 1994 2005b Although we were able to trip our subjects repeatedly, three of the fallers and one non-faller did not complete the whole protocol up to five tripping trials, due to discomfort. Nevertheless, the fallers fell in all trials and the non-faller did not fall in four tripping trials. Hence, the number of tripping trials did not affect the classification of the participants. 2005c 2005a 2005 2001 Fallers and non-fallers were classified in this study based on their ability to prevent a fall after an experimentally induced gait perturbation. In daily life, there are many various ways in which people fall. Furthermore, this experimental study included a small number of participants, which might have influenced the predictive values. A prospective study with a larger cohort is necessary to generalize the results of this study to the prediction of falls in daily life. 2005a c 1999 2004 1999 2003 2007 1999 2004 2003b 2007 2007 2001 2005 2003 2004 2004 2004 2004 Conclusion Relatively simple and accessible measures of maximum strength did identify elderly fallers from non-fallers after a standardized gait perturbation. The capacity to generate maximum extension force by the whole leg (e.g., in a leg press apparatus or during jumping) resulted in the best classification of older fallers and non-fallers. Follow up studies on larger cohorts with a wide range of muscle strength and walking velocities are necessary to generalize these results towards a valid prediction of fall risk.