Seasonal Changes in Heart Rate Variability in 11- to 13-Year-Old Girls

2005 ◽  
Vol 31 (4) ◽  
pp. 407-412 ◽  
Author(s):  
I. G. Kaisina ◽  
E. N. Sizova ◽  
V. I. Tsirkin ◽  
S. I. Trukhina
2012 ◽  
Vol 34 (05) ◽  
pp. 424-430 ◽  
Author(s):  
R. Oliveira ◽  
A. Leicht ◽  
D. Bishop ◽  
J. Barbero-Álvarez ◽  
F. Nakamura

Author(s):  
Eva Piatrikova ◽  
Nicholas J. Willsmer ◽  
Marco Altini ◽  
Mladen Jovanović ◽  
Lachlan J.G. Mitchell ◽  
...  

Purpose: First, to examine whether heart rate variability (HRV) responses can be modeled effectively via the Banister impulse-response model when the session rating of perceived exertion (sRPE) alone, and in combination with subjective well-being measures, are utilized. Second, to describe seasonal HRV responses and their associations with changes in critical speed (CS) in competitive swimmers. Methods: A total of 10 highly trained swimmers collected daily 1-minute HRV recordings, sRPE training load, and subjective well-being scores via a novel smartphone application for 15 weeks. The impulse-response model was used to describe chronic root mean square of the successive differences (rMSSD) responses to training, with sRPE and subjective well-being measures used as systems inputs. Changes in CS were obtained from a 3-minute all-out test completed in weeks 1 and 14. Results: The level of agreement between predicted and actual HRV data was R2 = .66 (.25) when sRPE alone was used. Model fits improved in the range of 4% to 21% when different subjective well-being measures were combined with sRPE, representing trivial-to-moderate improvements. There were no significant differences in weekly group averages of log-transformed (Ln) rMSSD (P = .34) or HRV coefficient of variation of Ln rMSSD (P = .12); however, small-to-large changes (d = 0.21–1.46) were observed in these parameters throughout the season. Large correlations were observed between seasonal changes in HRV measures and CS (changes in averages of Ln rMSSD: r = .51, P = .13; changes in coefficient of variation of Ln rMSSD: r = −.68, P = .03). Conclusion: The impulse-response model and data collected via a novel smartphone application can be used to model HRV responses to swimming training and nontraining-related stressors. Large relationships between seasonal changes in measured HRV parameters and CS provide further evidence for incorporating a HRV-guided training approach.


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