OBJECTIVE: Assessment of muscle strength is vital to the management of patients with muscular weakness. Clinical interpretation of isometric strength data for individual patients has been limited because of the lack of a reference population for comparison. The purpose of this study was to develop regression equations to predict maximal isometric strength based on gender, age, height, and weight. Patients' absolute strength values may then be expressed as a percentage of their predicted values, facilitating the determination of presence and extent of weakness. DESIGN: Three separate neuromuscular research groups developed databases of normal maximal isometric strength values, using standardized testing procedures. The databases were combined into a single database, and multiple regression equations were formulated for strength prediction for the 20 muscle groups tested. SETTING: Seven neuromuscular research units, each within the neurology department of a university-based teaching facility. SUBJECTS: A convenience sample of 493 volunteers who had no medical conditions that would have prohibited them from performing a maximal isometric strength test. MAIN OUTCOME MEASURE: Maximal isometric strength (kg) of ten muscle groups was measured bilaterally. RESULTS: Regression equations and 95% prediction intervals are derived from the combined database. A case study demonstrates the use of the predictive equations in determining presence and extent of weakness. CONCLUSION: Predictive strength equations facilitate assessment of muscular weakness.
OBJECTIVE: Assessment of muscle strength is vital to the management of patients with muscular weakness. Clinical interpretation of isometric strength data for individual patients has been limited because of the lack of a reference population for comparison. The purpose of this study was to develop regression equations to predict maximal isometric strength based on gender, age, height, and weight. Patients' absolute strength values may then be expressed as a percentage of their predicted values, facilitating the determination of presence and extent of weakness. DESIGN: Three separate neuromuscular research groups developed databases of normal maximal isometric strength values, using standardized testing procedures. The databases were combined into a single database, and multiple regression equations were formulated for strength prediction for the 20 muscle groups tested. SETTING: Seven neuromuscular research units, each within the neurology department of a university-based teaching facility. SUBJECTS: A convenience sample of 493 volunteers who had no medical conditions that would have prohibited them from performing a maximal isometric strength test. MAIN OUTCOME MEASURE: Maximal isometric strength (kg) of ten muscle groups was measured bilaterally. RESULTS: Regression equations and 95% prediction intervals are derived from the combined database. A case study demonstrates the use of the predictive equations in determining presence and extent of weakness. CONCLUSION: Predictive strength equations facilitate assessment of muscular weakness.
Authors: Craig M Zaidman; Elizabeth C Malkus; Catherine Siener; Julaine Florence; Alan Pestronk; Muhammad Al-Lozi Journal: Muscle Nerve Date: 2011-07-13 Impact factor: 3.217
Authors: Christopher Grunseich; Ilona R Kats; Laura C Bott; Carlo Rinaldi; Angela Kokkinis; Derrick Fox; Ke-Lian Chen; Alice B Schindler; Ami K Mankodi; Joseph A Shrader; Daniel P Schwartz; Tanya J Lehky; Chia-Ying Liu; Kenneth H Fischbeck Journal: Neuromuscul Disord Date: 2014-07-03 Impact factor: 4.296