title: Prediction of back strength using anthropometric and strength measurements in healthy females.
abstract: OBJECTIVE
  The purpose of this study was to develop a regression equation to predict back extensor maximal voluntary contraction (back strength) for females based on several anthropometric and strength measurements using a multiple regression technique.
  
  
  BACKGROUND
  Back strength is an important parameter in low back pain studies. However, the measurement of back strength is problematic in certain populations such as low back pain patients and pregnant women.
  
  
  METHODS
  Back strength was measured as both moment at L4/L5 and force. Ten anthropometric or strength measurements were chosen to develop the prediction equation. The data used for developing the models were from eighty non-pregnant female subjects, age 18-42 and with no history of back pain in the past year. Backwards stepwise analysis was performed in order to choose the best fit predictors. The predictive ability of each of the models was checked using the cross-validation technique on 20 other subjects.
  
  
  RESULTS
  Two prediction models were developed for moment and force, respectively. The models explained 46.9% and 48.2% of the variance in back strength. No multicollinearity problem was found. The validation study showed that the observed back strength was highly correlated with the predicted back strength.
  
  
  CONCLUSIONS
  Mass, height, trunk length, grip strength and quadriceps strength are the best predictors of back strength in this study. The models developed in this study can be used for both general female low back pain patients and the pregnancy population.
authors.affiliation: Department of Mechanical and Materials Engineering, Mclaughlin Hall, Queen's University, Kingston, ON, Canada K7L 3N6.
authors.forename: M
authors.initials: M
authors.lastname: Wang
authors.forename: A B
authors.initials: AB
authors.lastname: Leger
authors.forename: G A
authors.initials: GA
authors.lastname: Dumas
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D000886:Anthropometry / Q000379:methods*; D001415:Back / Q000502:physiology*; D001824:Body Constitution / Q000502:physiology*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005260:Female; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D018482:Muscle, Skeletal / Q000502:physiology*; D010807:Physical Endurance / Q000502:physiology*; D010809:Physical Fitness / Q000502:physiology*; D012044:Regression Analysis