Weekly Fluctuations in Internal Load and Neuromuscular Performance Across a 10-Week Training Period in Elite Female Boxers


AYDIN A., Altuğ T., Yılmaz C., Badau A., Söyler M.

Life, cilt.16, sa.3, 2026 (SCI-Expanded, Scopus) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 3
  • Basım Tarihi: 2026
  • Doi Numarası: 10.3390/life16030386
  • Dergi Adı: Life
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Directory of Open Access Journals
  • Anahtar Kelimeler: female boxers, neuromuscular performance, sRPE, training load, training monotony
  • Gazi Üniversitesi Adresli: Evet

Özet

This study examined weekly internal load and neuromuscular performance in elite junior female boxers over 10 weeks. Internal load was quantified using session rating of perceived exertion (sRPE), from which weekly monotony and strain were derived. Neuromuscular performance was assessed weekly using wall-sit endurance and a repetitive jump test. Twenty elite junior female boxers (Mean ± SD: 18.9 ± 1.2) were monitored during regular training without experimental manipulation. Weekly sRPE-derived training load, monotony, and strain showed statistically significant week-to-week fluctuations (p < 0.001). Neuromuscular performance improved in week 2, declined during weeks 3–5, and partially recovered in week 6. The findings demonstrated consistent temporal alignment between internal-load indices and week-to-week neuromuscular performance changes within an observational monitoring framework. Inter-individual variability was observed across athletes. Overall, sRPE-derived indices reflected training stress patterns and were aligned with neuromuscular performance changes in elite female boxers, supporting their use for contextual monitoring of weekly training responses. These findings support the practical integration of internal-load and performance monitoring in elite female combat-sport settings. Future research incorporating boxing-specific external-load metrics, physiological markers, and longer monitoring periods may further refine individualized load-management strategies.