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Objectives: The purpose of this study was to document the longitudinal strength and power characteristic changes and race performance changes of a skeleton athlete., Method: Longitudinal strength and power changes were assessed with strength and power diagnostic testing over a 9-year period. Trends over 9 years for relative strength were analyzed using a linear model. Push-start time was recorded across multiple tracks. Trends over 9 years for start performance at each track were assessed using a mixed-effects linear model to account for the impact of different tracks. Lower-body strength and power changes were assessed via a 1-repetition-maximum squat and a body-weight countermovement jump. The relationship between strength and power changes was assessed over time. The relationship between strength changes and start performance was determined by assessing the fixed effect of relative strength changes on push-start time., Results: Relative lower-body strength ranged from 1.6 kg per body weight to 1.9 kg per body weight and showed a significant mean improvement of 0.05 kg per body weight per year (R2 = .71, P < .01). A negative correlation (R2 = .79) between relative strength changes and push-start performance across multiple tracks was found. The mixed-effects model indicated that push-start time improved significantly year to year (0.02 s; P < .001; R2 = .74) when controlling for the effect of track., Conclusions: The longitudinal analysis of push-start time and the associations with changes in strength suggest that training this quality can have a positive effect on push-start performance.

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.

Purpose: Critical speed (CS) and supra-CS distance capacity (D') are useful metrics for monitoring changes in swimmers' physiological and performance capacities. However, the utility of these metrics across a season has not been systematically evaluated in high level swimmers., Methods: Twenty-seven swimmers (18 female; age 19.1 ± 2.9 y, 9 male; 19.5 ± 1.9 y, mean ± SD) completed the 12x25m swimming test multiple times (4 ± 3 tests/swimmer) across a two-year period. Season-best times in all distances for the test stroke were sourced from publicly available databases. Swimmers' distance speciality was determined as the event with the time closest to world record. Four metrics were calculated from the 12x25m test: CS, D', peak speed and drop off %., Results: Guyatt's Responsiveness Index values were calculated to ascertain the practically relevant sensitivity of each 12x25m metric: CS = 1.5, peak speed = 2.3, D' = 2.1 and drop off % = 2.6. These values are modified effect sizes (ES); all are large effects. Bayesian mixed-modelling showed substantial between-subject differences between genders and strokes for each variable, but minimal within-subject changes across the season. Drop off % was lower in 200 m swimmers (14.0 ± 3.3%) compared to 100 m swimmers (18.1 ± 4.1%, p = 0.003, ES = 1.10)., Conclusion: The 12x25m test is best suited to differentiating between swimmers of different strokes and events. Further development is needed to improve its utility in quantifying meaningful changes over a season for individual swimmers.