Abstract
Background and objective: Identifying methods of accurately predicting endurance capacity of the back extensor muscles is of immense value to both patients and clinicians. This study aimed to develop a multiple regression equation using demographic and anthropometric measurements to predict back endurance and to investigate the association between performed back endurance results and back endurance predictions.
Materials and methods: 376 healthy women and men aged 21–62 y were recruited consecutively. All participants underwent the Biering-Sørenson test of Static Muscular Endurance. Height and weight were measured using standard procedures. Percentage body fat was estimated using bioelectrical impedance analysis. Body mass index, lean body mass and body fat mass were calculated. Based on these a regression model was used to generate predictive equation for endurance time.
Results: The final regression model which included demographic and anthropometric variables could account for 43% of the variability in back muscle endurance. Pearson's product correlation coefficient for the association between performed static endurance and the predicted endurance were(r = −0.129; p = 0.074) and (r = 0.671; p = 0.000) for male and female participants respectively. Paired t-test showed significant difference (p = 0.000) between the performed static endurance and predicted endurance among the male participants but not among the female counterparts (p = 0.959). Endurance time of males (119 ± 49.8 secs.) was significantly greater (p = 0.014) than that of the females (106 ± 47.6 secs.).
Conclusion: Regression equation seems to be moderately valid in predicting endurance time of the back extensors among females only. We conclude that simple anthropometric measures cannot accurately predict back endurance without significant errors. The larger error of prediction obtained for the male participants could be indicative of an increasing influence of motivational/psychosocial factors that are known to be independent of motor ability in physical performance testing.