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Purpose: Quantifying training intensity provides a comprehensive understanding of the training stimulus. Recent technological advances may have improved the feasibility of using heart-rate (HR) monitoring in swimming. However, the implementation of HR monitoring is yet to be assessed longitudinally in the daily training environment of swimmers. This study aimed to assess the implementation of HR by comparing the training-intensity distribution from an external measure, planned volume at set intensities (PVSI), with the internal training-intensity distribution measured using time in HR zones. Methods: Using a longitudinal observational design, 10 competitive swimmers (8 male and 2 female, age: 22.0 [2.3] y, Fédération Internationale de Natation point score: 842.9 [58.5], mean [SD]) were monitored daily for 6 months. Each session, HR data, and coached-planned and athlete-reported session rating of perceived exertion (Modified Category Ratio 10 scale) were recorded. Based on previously determined training zones from an incremental step test, PVSI was calculated using the planned distance and planned intensity of each swim bout. Training-intensity distributions were analyzed using a linear mixed model (lme4). Results: The model revealed a small to moderate relationship between PVSI and time in HR zone, based on the Nakagawa R-squared value (range .14–.42). Conclusions: Training-intensity distribution differed between the internal measure (ie, HR) and the external measure of intensity (ie, PVSI). This demonstrates that internal and planned external measures of intensity cannot be used interchangeably to monitor training. Further research should explore how to best integrate these measures to better understand training in swimming.

Purpose: To assess a coach’s subjective assessment of their athletes’ performances and whether the use of athlete-monitoring tools could improve on the coach’s prediction to identify performance changes. Methods: Eight highly trained swimmers (7 male and 1 female, age 21.6 [2.0] y) recorded perceived fatigue, total quality recovery, and heart-rate variability over a 9-month period. Prior to each race of the swimmers’ main 2 events, the coach (n = 1) was presented with their previous race results and asked to predict their race time. All race results (n = 93) with aligning coach’s predictions were recorded and classified as a dichotomous outcome (0 = no change; 1 = performance decrement or improvement [change +/− > or < smallest meaningful change]). A generalized estimating equation was used to assess the coach’s accuracy and the contribution of monitoring variables to the model fit. The probability from generalized estimating equation models was assessed with receiver operating characteristic curves to identify the model’s accuracy from the area under the curve analysis. Results: The coach’s predictions had the highest diagnostic accuracy to identify both decrements (area under the curve: 0.93; 95% confidence interval, 0.88–0.99) and improvements (area under the curve: 0.89; 95% confidence interval, 0.83–0.96) in performance. Conclusions: These findings highlight the high accuracy of a coach’s subjective assessment of performance. Furthermore, the findings provide a future benchmark for athlete-monitoring systems to be able to improve on a coach’s existing understanding of swimming performance.

Purpose: To assess the efficacy of a combined light exposure and sleep hygiene intervention to improve team-sport performance following eastward long-haul transmeridian travel. Methods: Twenty physically trained males underwent testing at 09:00 and 17:00 hours local time on 4 consecutive days at home (baseline) and the first 4 days following 21 hours of air travel east across 8 time zones. In a randomized, matched-pairs design, participants traveled with (INT; n = 10) or without (CON; n = 10) a light exposure and sleep hygiene intervention. Performance was assessed via countermovement jump, 20-m sprint, T test, and Yo-Yo Intermittent Recovery Level 1 tests, together with perceptual measures of jet lag, fatigue, mood, and motivation. Sleep was measured using wrist activity monitors in conjunction with self-report diaries. Results: Magnitude-based inference and standardized effect-size analysis indicated there was a very likely improvement in the mean change in countermovement jump peak power (effect size 1.10, ±0.55), and likely improvement in 5-m (0.54, ±0.67) and 20-m (0.74, ±0.71) sprint time in INT compared with CON across the 4 days posttravel. Sleep duration was most likely greater in INT both during travel (1.61, ±0.82) and across the 4 nights following travel (1.28, ±0.58) compared with CON. Finally, perceived mood and motivation were likely worse (0.73, ±0.88 and 0.63, ±0.87) across the 4 days posttravel in CON compared with INT. Conclusions: Combined light exposure and sleep hygiene improved speed and power but not intermittent-sprint performance up to 96 hours following long-haul transmeridian travel. The reduction of sleep disruption during and following travel is a likely contributor to improved performance.

Purpose: To examine whether concurrent heat and intermittent hypoxic training can improve endurance performance and physiological responses relative to independent heat or temperate interval training. Methods: Well-trained male cyclists (N = 29) completed 3 weeks of moderate- to high-intensity interval training (4 × 60 min·wk⁻¹) in 1 of 3 conditions: (1) heat (HOT: 32°C, 50% relative humidity, 20.8% fraction of inspired oxygen, (2) heat + hypoxia (H+H: 32°C, 50% relative humidity, 16.2% fraction of inspired oxygen), or (3) temperate environment (CONT: 22°C, 50% relative humidity, 20.8% fraction of inspired oxygen). Performance 20-km time trials (TTs) were conducted in both temperate (TT_temperate) and assigned condition (TT_environment) before (base), immediately after (mid), and after a 3-week taper (end). Measures of hemoglobin mass, plasma volume, and blood volume were also assessed. Results: There was improved 20-km TT performance to a similar extent across all groups in both TT_temperate (mean ±90% confidence interval HOT, −2.8% ±1.8%; H+H, −2.0% ±1.5%; CONT, −2.0% ±1.8%) and TT_environment (HOT, −3.3% ±1.7%; H+H, −3.1% ±1.6%; CONT, −3.2% ±1.1%). Plasma volume (HOT, 3.8% ±4.7%; H+H, 3.3% ±4.7%) and blood volume (HOT, 3.0% ±4.1%; H+H, 4.6% ±3.9%) were both increased at mid in HOT and H+H over CONT. Increased hemoglobin mass was observed in H+H only (3.0% ±1.8%). Conclusion: Three weeks of interval training in heat, concurrent heat and hypoxia, or temperate environments improve 20-km TT performance to the same extent. Despite indications of physiological adaptations, the addition of independent heat or concurrent heat and hypoxia provided no greater performance benefits in a temperate environment than temperate training alone.