Abstract
Shoulder fatigue is a key contributor to performance decline and injury risk in swimmers, highlighting the need for objective monitoring tools; yet, despite their widespread use in land-based sports, wearable sensors remain underutilized in aquatic environments. PURPOSE: To examine how wearable muscle oxygenation (SmO2%) sensors can quantify local fatigue during performance, predict fatigue-induced kinematic breakdowns, and inform evidence-based training strategies. METHODS: This IRB-approved study (Lehigh University #2113291-4) recruited fourteen (n=14) Division I butterfly swimmers randomized into Exercise (EX) or Control (CON) groups. All athletes maintained standard training routines, while EX completed a targeted exercise—two sets to failure, twice weekly for 16 weeks—aimed at improving the butterfly recovery phase. Pre- and post-intervention testing involved a 200-yard butterfly swim. MOXY sensors were placed bilaterally on the posterior deltoids to record SmO2% at 1 Hz, and an underwater camera at the 5-yard mark captured body position on the final lap. Trunk Inclination (TI) angle was measured in Kinovea at the completion of the recovery phase. Linear regression examined the relationship between ΔSmO2% and ΔTI, and one-sided t-tests (p < 0.05) identified significant SmO2% decreases. RESULTS: Five of seven athletes in the exercise (EX) group demonstrated a significant decrease in SmO2% (p < 0.01) accompanied by an increase in TI. None of the control (CON) group athletes exhibited a significant change in SmO2%. A strong, significant correlation was observed between changes in TI and SmO2% (R2 = 0.94, p < 0.01), irrespective of athlete grouping. CONCLUSION: Significant reductions in SmO2% levels, coupled with increases in TI among participants in the exercise group, suggest that these technologies are valuable tools for assessing training effectiveness and their influence on aquatic athlete performance. Future research should focus on integrating SmO2% data with additional body positioning metrics to develop predictive models of biomechanical efficiency across various strokes. Such models could enable coaches to anticipate and detect fatigue-related technique breakdowns, optimize training protocols, and refine stroke mechanics with greater precision to reduce injury risk.
Recommended Citation
Grotke, Jack; Rashkovsky, Gerald; and Seshadri, Dhruv
(2025)
"Turning Aquatic Wearable Research into Coaching Insight: Quantifying Fatigue to Improve Technique,"
International Journal of Exercise Science: Conference Proceedings: Vol. 15:
Iss.
7, Article 32.
Available at:
https://digitalcommons.wku.edu/ijesab/vol15/iss7/32