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Intra-Session Reliability and Predictive Value of Maximum Voluntary Isometric Contraction for Estimating
José Aldo Hernández-Murúa1, Ena Monserrat Romero-Pérez2, Jorge Luis Guajardo-Cruztitla1
1Faculty of Physical Education and Sports, Autonomous University of Sinaloa (UAS), Culiacán 80013, Mexico.
Maximum voluntary isometric contraction (MVIC) is reliable for assessing muscle strength in older women. However, its ability to predict one-repetition maximum (1RM) is limited, requiring further research for improved models.
Area of Science:
- Gerontology
- Exercise Physiology
- Biomedical Engineering
Background:
- Aging leads to decreased muscle strength, impacting functional independence in older adults.
- Assessing lower limb muscle strength is crucial for maintaining mobility and quality of life.
- Existing methods like maximum voluntary isometric contraction (MVIC) and one-repetition maximum (1RM) have limitations in older populations.
Purpose of the Study:
- To determine the intra-session reliability of MVIC for knee extensors in older women.
- To analyze the correlation between MVIC and 1RM in this demographic.
- To develop a predictive model for estimating 1RM from MVIC.
Main Methods:
- A randomized split-sample design was used with 82 women aged 60-69 years.
- Participants performed two MVIC trials and one 1RM test on a leg extension machine.
- Intraclass correlation coefficient (ICC), SEM, and MDC were calculated for reliability; linear regression for prediction.
Main Results:
- MVIC showed excellent intra-session reliability (ICC=0.96, SEM=4.3%, MDC=11.9%).
- A strong correlation was found between MVIC and 1RM (R²=0.618).
- The predictive equation (1RM = [0.932 × MVIC] - 3.852) had a prediction error of 13.4%.
Conclusions:
- MVIC is a highly reliable and practical measure for assessing knee extensor strength in older women.
- While MVIC is reliable, its predictive accuracy for 1RM is currently limited.
- Further research is needed to enhance predictive models by including additional physiological variables.
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