Effects of motor imagery training on muscle strength and co-contraction for older adults

ObjectiveThis pilot study aimed to evaluate the effectiveness of motor imagery training (MIT) in enhancing maximal voluntary contraction (MVC) force among healthy older adults and inducing neural adaptations measured by electromyography (EMG) signals of both agonist and antagonist muscles, as well a...

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Main Authors: Wan X. Yao, Bernadett Mamone, Sha Ge, Mehmed B. Bayram, Jiang Wu, Bo Y. Jiang, John Q. Zhang, Guang H. Yue
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-07-01
Series:Frontiers in Psychology
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Online Access:https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1441377/full
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Summary:ObjectiveThis pilot study aimed to evaluate the effectiveness of motor imagery training (MIT) in enhancing maximal voluntary contraction (MVC) force among healthy older adults and inducing neural adaptations measured by electromyography (EMG) signals of both agonist and antagonist muscles, as well as the co-contraction index (CCI).MethodsConducted with a single MIT group using a within-subject design, the study involved 12 right-handed elderly participants, with nine completing the 8-week training (5 sessions/week and 30 min/session). Elbow flexion MVC force and MVC EMG for biceps brachii (agonist) and triceps brachii (antagonist) were recorded pre- and post-training.ResultsSignificant improvements in muscle strength were observed following the 8-week MIT, with a notable 22% increase (p < 0.05). Additionally, there was a significant 27% increase in EMG amplitude for the agonist muscle (p < 0.05), with no notable change in EMG amplitude for the antagonist muscle. Notably, this study is the first to demonstrate a significant decrease in CCI (−32%, p < 0.05) following MIT.ConclusionThese findings provide further evidence of MIT’s efficacy in enhancing voluntary muscle strength through neural adaptations, particularly beneficial and safer for older individuals encountering challenges with conventional strength training methods.
ISSN:1664-1078