Standard isometric contraction has higher reliability than maximum voluntary isometric contraction for normalizing electromyography during level walking among older adults with knee osteoarthritis

Front Bioeng Biotechnol. 2024 Feb 16:12:1276793. doi: 10.3389/fbioe.2024.1276793. eCollection 2024.

Abstract

Introduction: Electromyography (EMG) normalization often relies on maximum voluntary isometric contraction (MVIC), which may not be suitable for knee osteoarthritis (KOA) patients due to difficulties in generating maximum joint torques caused by pain. This study aims to assess the reliability of standard isometric contraction (SIC) for EMG normalization in older adults with KOA, comparing it with MVIC. Methods: We recruited thirty-five older adults with KOA and collected root mean square EMG amplitudes from seven muscles in the affected limb during level walking, SIC, and MVIC tests. EMG data during level walking were normalized using both SIC and MVIC methods. This process was repeated after at least 1 week. We calculated intra-class correlation coefficients (ICCs) with 95% confidence intervals to evaluate between- and within-day reliabilities. Results: SIC tests showed higher between- (ICC: 0.75-0.86) and within-day (ICC: 0.84-0.95) ICCs across all seven muscles compared to MVIC tests. When normalized with SIC, all seven muscles exhibited higher between- (ICC: 0.67-0.85) and within-day (ICC: 0.88-0.99) ICCs compared to MVIC normalization. Conclusion: This study suggests that SIC may offer superior movement consistency and reliability compared to MVIC for EMG normalization during level walking in older adults with KOA.

Keywords: EMG normalization; MVIC; gait; knee osteoarthritis; reliability.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This project was funded by General Administration of Sport of China (23QN009), National Natural Science Foundation of China (No.12102235), and Natural Science Foundation of Shandong Province (ZR2022MH163).