Search Results

Purpose: To investigate the effect of a 5-day short-term heat acclimation (STHA) protocol in dry (43°C and 20% relative humidity) or humid (32°C and 80% relative humidity) environmental conditions on endurance cycling performance in temperate conditions (21°C). Methods: In a randomized, cross-over design, 11 cyclists completed each of the two 5-day blocks of STHA matched for heat index (44°C) and total exposure time (480 min), separated by 30 days. Pre- and post-STHA temperate endurance performance (4-min mean maximal power, lactate threshold 1 and 2) was assessed; in addition, a heat stress test was used to assess individual levels of heat adaptation. Results: Differences in endurance performance were unclear. Following dry STHA, gross mechanical efficiency was likely reduced (between-condition effect size dry vs humid −0.59; 90% confidence interval, −1.05 to −0.15), oxygen uptake was likely increased for a given workload (0.64 [0.14 to 1.07]), and energy expenditure likely increased (0.59 [0.17 to 1.03]). Plasma volume expansion at day 5 of acclimation was similar (within-condition outcome 4.6% [6.3%] and 5.3% [5.1%] dry and humid, respectively) but was retained for 3 to 4 days longer after the final humid STHA exposure (−0.2% [8.1%] and 4.5% [4.2%] dry and humid, respectively). Sweat rate was very likely increased during dry STHA (0.57 [0.25 to 0.89]) and possibly increased (0.18 [−0.15 to 0.50]) during humid STHA. Conclusion: STHA induced divergent adaptations between dry and humid conditions, but did not result in differences in temperate endurance performance.

Purpose: To prescribe training loads to improve performance, one must know how an athlete is responding to loading. The maximal rate of heart-rate increase (rHRI) during the transition from rest to exercise is linearly related to changes in endurance exercise performance and can be used to infer how athletes are responding to changes in training load. Relationships between rHRI and anaerobic exercise performance have not been evaluated. The objective of this study was to evaluate relationships between rHRI and anaerobic exercise performance. Methods: Eighteen recreational strength and power athletes (13 male and 5 female) were tested on a cycle ergometer for rHRI, 6-second peak power output, anaerobic capacity (30-s average power), and blood lactate concentration prior to (PRE), and 1 (POST1) and 3 (POST3) hours after fatiguing high-intensity interval cycling. Results: Compared with PRE, rHRI was slower at POST1 (effect size [ES] = −0.38, P = .045) but not POST3 (ES = −0.36, P = .11). PPO was not changed at POST1 (ES = −0.12, P = .19) but reduced at POST3 (ES = −0.52, P = .01). Anaerobic capacity was reduced at POST1 (ES = −1.24, P < .001) and POST3 (ES = −0.83, P < .001), and blood lactate concentration was increased at POST1 (ES = 1.73, P < .001) but not at POST3 (ES = 0.75, P = .11). rHRI was positively related to PPO (B = 0.19, P = .03) and anaerobic capacity (B = 0.14, P = .005) and inversely related to blood lactate concentration (B = −0.22, P = .04). Conclusions: rHRI is linearly related to acute changes in anaerobic exercise performance and may indicate how athletes are responding to training to guide the application of training loads.

Purpose: There are important methodological considerations for translating wearable-based gait-monitoring data to field settings. This study investigated different devices’ sampling rates, signal lengths, and testing frequencies for athlete monitoring using dynamical systems variables. Methods: Secondary analysis of previous wearables data (N = 10 runners) from a 5-week intensive training intervention investigated impacts of sampling rate (100–2000 Hz) and signal length (100–300 strides) on detection of gait changes caused by intensive training. Primary analysis of data from 13 separate runners during 1 week of field-based testing determined day-to-day stability of outcomes using single-session data and mean data from 2 sessions. Stride-interval long-range correlation coefficient α from detrended fluctuation analysis was the gait outcome variable. Results: Stride-interval α reduced at 100- and 200- versus 300- to 2000-Hz sampling rates (mean difference: −.02 to −.08; P ≤ .045) and at 100- compared to 200- to 300-stride signal lengths (mean difference: −.05 to −.07; P < .010). Effects of intensive training were detected at 100, 200, and 400 to 2000 Hz (P ≤ .043) but not 300 Hz (P = .069). Within-athlete α variability was lower using 2-session mean versus single-session data (smallest detectable change: .13 and .22, respectively). Conclusions: Detecting altered gait following intensive training was possible using 200 to 300 strides and a 100-Hz sampling rate, although 100 and 200 Hz underestimated α compared to higher rates. Using 2-session mean data lowers smallest detectable change values by nearly half compared to single-session data. Coaches, runners, and researchers can use these findings to integrate wearable-device gait monitoring into practice using dynamic systems variables.