Intra-Session Reliability and Predictive Value of Maximum Voluntary Isometric Contraction for Estimating One-Repetition Maximum in Older Women: A Randomised Split-Sample Study.
José Aldo Hernández-Murúa, Ena Monserrat Romero-Pérez, Jorge Luis Guajardo-Cruztitla, Blas Sinahí Madrigal Olivares, Ángel Gallego-Selles, Diego González-Martín, Francisca Reyes-Merino, Nidia Sánchez-García, José Antonio de Paz
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Abstract
Background: Ageing is associated with a progressive decline in muscle strength, particularly in the lower limbs, which compromises functional independence. While both maximum voluntary isometric contraction (MVIC) and one-repetition maximum (1RM) are widely employed to assess muscle strength, the intra-session reliability and predictive capacity of MVIC for estimating 1RM in older women remain insufficiently explored. Objectives: This study aims to evaluate the intra-session reliability of MVIC in knee extensors, analyse its correlation with 1RM, and develop a predictive model for estimating 1RM from MVIC in older women. Methods: Using a randomised split-sample design, 82 women aged 60-69 years performed two MVIC trials and one 1RM test using a leg extension machine. Intra-session reliability was assessed by calculating the intraclass correlation coefficient (ICC), the standard error of measurement (SEM), and the minimal detectable change (MDC). Furthermore, a linear regression model was developed to predict 1RM based on MVIC. Results: MVIC demonstrated excellent intra-session reliability (ICC = 0.96, SEM = 4.3%, MDC = 11.9%), and a strong correlation between MVIC and 1RM was observed (R2 = 0.618). Although the predictive equation 1RM = [(0.932 × MVIC) - 3.852] did not yield statistically significant differences between the estimated and actual 1RM values (p = 0.791), it exhibited a prediction error of 13.4%. Conclusions: MVIC is a highly reliable measure in older women and represents a practical tool for estimating 1RM. Nonetheless, its predictive accuracy is limited, highlighting the need for further studies to refine predictive models by incorporating additional variables.