, speed, and direction) during training. 3 Traditionally, video analysis is used within swimming as the gold standard criterion, 5 to quantitatively measure various swimming metrics (eg, stroke count, velocity, and technical proficiency). 4 , 6 However, video analysis is laborious, does not allow real
Stephanie J. Shell, Brad Clark, James R. Broatch, Katie Slattery, Shona L. Halson, and Aaron J. Coutts
Eva Piatrikova, Nicholas J. Willsmer, Ana C. Sousa, Javier T. Gonzalez, and Sean Williams
single pull-out). The stroke parameters and speed were measured in the last 3 laps. Based on previous findings, 8 , 9 the SR associated with the closest 50-m time to CS was used as a CSR (SR@CS) and stroke count (SC@CS) estimates. In most swimmers, this corresponded to the third or fourth lap, where
Alistair P. Murphy, Rob Duffield, Aaron Kellett, and Machar Reid
To investigate the discrepancy between coach and athlete perceptions of internal load and notational analysis of external load in elite junior tennis.
Fourteen elite junior tennis players and 6 international coaches were recruited. Ratings of perceived exertion (RPEs) were recorded for individual drills and whole sessions, along with a rating of mental exertion, coach rating of intended session exertion, and athlete heart rate (HR). Furthermore, total stroke count and unforced-error count were notated using video coding after each session, alongside coach and athlete estimations of shots and errors made. Finally, regression analyses explained the variance in the criterion variables of athlete and coach RPE.
Repeated-measures analyses of variance and interclass correlation coefficients revealed that coaches significantly (P < .01) underestimated athlete session RPE, with only moderate correlation (r = .59) demonstrated between coach and athlete. However, athlete drill RPE (P = .14; r = .71) and mental exertion (P = .44; r = .68) were comparable and substantially correlated. No significant differences in estimated stroke count were evident between athlete and coach (P = .21), athlete notational analysis (P = .06), or coach notational analysis (P = .49). Coaches estimated significantly greater unforced errors than either athletes or notational analysis (P < .01). Regression analyses found that 54.5% of variance in coach RPE was explained by intended session exertion and coach drill RPE, while drill RPE and peak HR explained 45.3% of the variance in athlete session RPE.
Coaches misinterpreted session RPE but not drill RPE, while inaccurately monitoring error counts. Improved understanding of external- and internal-load monitoring may help coach–athlete relationships in individual sports like tennis avoid maladaptive training.
Louise Martin, Alan M. Nevill, and Kevin G. Thompson
Fast swim times in morning rounds are essential to ensure qualification in evening finals. A significant time-of-day effect in swimming performance has consistently been observed, although physical activity early in the day has been postulated to reduce this effect. The aim of this study was to compare intradaily variation in race-pace performance of swimmers routinely undertaking morning and evening training (MEG) with those routinely undertaking evening training only (EOG).
Each group consisted of 8 swimmers (mean ± SD: age = 15.2 ± 1.0 and 15.4 ± 1.4 y, 200-m freestyle time 132.8 ± 8.4 and 136.3 ± 9.1 s) who completed morning and evening trials in a randomized order with 48 h in between on 2 separate occasions. Oral temperature, heart rate, and blood lactate were assessed at rest, after a warm-up, after a 150-m race-pace swim, and after a 100-m time trial. Stroke rate, stroke count, and time were recorded for each length of the 150-m and 100-m swims.
Both training groups recorded significantly slower morning 100-m performances (MEG = +1.7 s, EOG = +1.4 s; P < .05) along with persistently lower morning temperatures that on average were –0.47°C and –0.60°C, respectively (P < .05). No differences were found in blood-lactate, heart-rate, and stroke-count responses (P > .05). All results were found to be reproducible (P > .05).
The long-term use of morning training does not appear to significantly reduce intradaily variation in race-pace swimming or body temperature.
Anne Z. Beethe, Elizabeth F. Nagle, Mita Lovalekar, Takashi Nagai, Bradley C. Nindl, and Christopher Connaboy
, and any other mission-specific gear, directly on body for easy access. Kounalakis et al 33 observed FS with fatigues, reporting decreased velocity and increased stroke count with similar VO 2 max among Army Officer Cadets compared with no fatigues performance. Yet adaptations due to constraints with
Kosuke Kojima, Christopher L. Brammer, Tyler D. Sossong, Takashi Abe, and Joel M. Stager
than 5 strokes to the stroke count of their initial repetition, and/or 3) when calculated power output declined in 2 successive trials. Training Protocols Swimmers assigned to the RST performed resisted sprint swim training using the modified Power Rack twice per week for 10 weeks (Mondays and
Joffrey Drigny, Marine Rolland, Robin Pla, Christophe Chesneau, Tess Lebreton, Benjamin Marais, Pierre Outin, Sébastien Moussay, Sébastien Racinais, and Benoit Mauvieux
speed on each lap, and the repeated correlation analysis was based on the association between the changes in speed and temperature over each lap. Further research on the association between speed and T core might use global positioning system devices for stroke count quantification and continuous