{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T14:44:15Z","timestamp":1787237055940,"version":"build-2736575974"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:00:00Z","timestamp":1772668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to investigate the effects of fatigue on the control mechanism of the lower limb neuromuscular system during running.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The study participants were 25 male running enthusiasts (age: 20.9 \u00b1 1.6 years; height: 174.8 \u00b1 4.5 cm; body mass: 70.3 \u00b1 5.2 kg) with more than 3 years of running experience. Surface electromyography (sEMG), motion capture, and heart rate monitoring technologies were used to obtain data for analysis in three dimensions: muscle activation, joint co-activation, and muscle synergy. The participants began running at 8 km\/h, with the speed increased by 1 km\/h every 2 min until their heart rate reached 75% of the estimated maximum heart rate (MHR), after which an individualized constant-speed phase was performed. Peak fatigue state was determined by two criteria: heart rate reaching 90% of MHR and a Borg scale score of \u226517. Data were compared before and after fatigue. Muscle synergies were extracted using non-negative matrix factorization (NNMF), and the number of modules was determined when the variance accounted for (VAF) first reached 95%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The results showed that muscle activation exhibited phase-specific changes after fatigue: During the stance phase, the root mean square (RMS) values of the quadriceps femoris (QF) and gluteus maximus (GM) increased significantly (p &amp;lt; 0.05). During the swing phase, the RMS value of the tibialis anterior (TA) increased significantly while that of the GM decreased significantly (p &amp;lt; 0.05). The joint co-activation ratio (CAR) increased significantly only at the ankle joint during the swing phase (p &amp;lt; 0.05). There was no significant difference in the number of muscle synergy modules (p &amp;gt; 0.05). However, the activation timing of the early stance module was significantly advanced (p &amp;lt; 0.05), and the muscle weights within the modules also changed; for example, the weights of the vastus lateralis (VL) and lateral head of the gastrocnemius (GL) decreased, while the weight of the TA increased (p &amp;lt; 0.05).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our results indicate that under running-induced fatigue, the lower limb neuromuscular system maintains motor output based on the principle of \u201cstability first\u201d through three approaches: \u201cselective compensation\u201d to activate muscles, \u201crigidity enhancement\u201d to stabilize distal joints, and \u201ctiming adjustment\u201d to optimize synergy modules. The findings may contribute to training optimization and help reduce the likelihood of potential injuries.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphys.2026.1741432","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T05:25:33Z","timestamp":1772688333000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Effects of fatigue on the activation characteristics and synergistic patterns of lower limb muscles during running"],"prefix":"10.3389","volume":"17","author":[{"given":"Zihao","family":"Li","sequence":"first","affiliation":[{"name":"Department of Track and Field, Institute of Physical Education and Training, Capital University of Physical Education and Sports","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"He","sequence":"additional","affiliation":[{"name":"Sports Department, Beijing University of Aeronautics and Astronautics","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,5]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"939042","DOI":"10.3389\/fphys.2022.939042","article-title":"Effects of neuromuscular training on athletes physical fitness in sports: a systematic review","volume":"13","author":"Akbar","year":"2022","journal-title":"Front. 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