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Published on: January 28, 2020
Four-Week Exponentially Weighted Chronic Load Derived From Session-Rating of Perceived Exertion Reflects Fitness in
Ayaka Takegami1, Naruto Yoshida1, Yoshio Nakata2
1College of Sport and Wellness,Rikkyo University, Niiza, Saitama, Japan.
Purpose:
To determine the appropriate period and calculation method for the chronic load (CL) that reflects physical fitness in female university handball players, by investigating the relationship between fitness test performance and CL using different calculation periods and methods.
Methods:
The participants were 24 female handball players from a first-division university league. Daily session-rating of perceived exertion measurements and weekly fitness tests (5-step jump, countermovement jump, and Edgren side-step) were conducted over a period of approximately 7 months. CL was calculated from session-rating of perceived exertion by period (1-4 wk), and calculation method (rolling average [RA], exponentially weighted moving average [EWMA]), and the relationship between each CL and the fitness test was analyzed using linear mixed-effects models with random intercepts and slopes. The Akaike information criterion and marginal/conditional R2 was used to evaluate model fit and the influence of individual variability.
Results:
For the 5-step jump, the EWMA4W model exhibited the lowest Akaike information criterion compared to the null model. EWMA4W models showed highest positive fixed-effect estimates (0.148; 95% confidence intervals, 0.087 to 0.209) for the 5-step jump (P < .05). However, the marginal R2 (.061) was substantially lower than the conditional R2 (.847), indicating that while CL significantly influences performance, individual-specific factors explain the majority of the variance. No significant relationships were observed for the countermovement jump or Edgren side-step (P ≥ .05).
Conclusions:
EWMA4W is a sensitive indicator for tracking long-term fitness adaptations in explosive power in female university handball players. The high individual variability underscores the necessity of individualized monitoring.

